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NUREGCR2220Vl 

EI IMPACT OF ENTRAINMENT AND IMPINGEMENT 

FISH POPULATIONS IN THE HUDSON RIVER 

NARY. VOLUME I: ENTRAINMENT IMPACT 

ESTIMATES FOR SIX FISH POPULATIONS 

INHABITING THE HUDSON RIVER ESTUARY 

OAK RIDGE NATIONAL LAB., TN 

JAN 1982 

US. DEPARTMENT OFZOMMERCE 

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NURRG CR2220V1

111 llllllllllClllllllllliillllllllll~llllll111 

Volume I 

ORNL/NUREG/TM-385/Vl 

Distribution Category-RE 

Contract No. W-7405-eng-26 

THE IMPACT OF ENTRAINMENT AND IMPINGEMENT ON 

FISH POPULATIONS IN THE HUDSON RIVER ESTUARY 

VOLUME I 

Entrainment Impact Estimates For Six Fish Populations 

Inhabiting the Hudson River Estuary 

J. Boreman,l L. W. Barnthouse, D. S. Vaughan, 

C. P. Goodyear,:! S. W. Christensen, K. D. Kumar, 

B. L. Kirk, and W. Van Winkle 

ENVIRONMENTAL SCIENCES DIVISION 

Publication No. 1790 

INortheast Fisheries Center , National Marine Fisheries Service, 

Woods Hole, Massachusetts 02543. 

2National Fish Center--Leetown, U.S. Fish and Wildlife Service, 

Route 3, P.O. Box 41, Kearneysville, West Virginia 25430. 

Manuscript Completed - September 1981 

Date Published - January 1982 

Prepared for 

Office of Nuclear Regulatory Research 

U.S. Nuclear Regulatory Commission 

Washington, D.C 20555 

Under Interagency Agreement No. DOE 40-550-75 

NRC FIN BO165 

TASK: Methods to Assess Impacts on Hudson River Striped Bass 

OAK RIDGE NATIONAL LABORATORY 

Oak Ridge, Tennessee 37830 

operated by 

UNION CARBIDE CORPORATION 

for the 

DEPARTMENT OF ENERGY 

REPRODUCEDBY 

US. DEPARTMENT OF COMMERCE 

NATIOt4P.LTECHNICAL 

INFORMATION SERVICE 

SPRINGFIELD,VA 22161 
BIBLIOGRAPHIC INFORMATION 

NUREG/CR-2220-V I 

The Impact of Entrainment and Implingement on Fish 

Populations in the Hudson River Estuary. Volume I: 

Entrainment Impact Estimates for Six Fish Populations

Inhabiting the Hudson River Estuary, 

Jan 82 

J. Boreman, L. U. Barnthouse, D. S. Vaughan, C. P. Goodyear,

and S. W. Christensen. 

PERFORMER: Oak Ridge National Lab., TN. 

ORNL/N U REG / TM -385 /B 1 

Contract W-7405-eng-26 

SPONSOR: Nuclear Regulatory Commission, Washington, DC. 

Off ice -of Hucleer Regulatory Re search . 

Pub. as Environmental Sciences Div. Pub-1790. Prepared in 

cooperation with National Marine Fisheries Service, Woods 

Hole, MA. Northeast Fisheries Center, and Fish and Nildlife 

Service, Kearneysville, WV. 

The report is concerned with the estimation of the direct 

(or annual) entrainment impact of the power plants on ' 

populations of striped bass, white perch, Alosa spp. 

(blueback herring and alewife), American shad, Atlantic 

tomcod, and bay anchovy in the Hudson River. Entrainment 

impact results from the killing of fish eggs, larvae, and 

young juveniles that are contained in the cooling water 

cycled through a power plant. An 'Empirical Transport Model' 

is presented as the means of obtaining a conditional 

entrainment mortality rate. 

KEYWORDS: *Entrainment, "Fishes, "Intake systems, *Hudson 

River Estuary, "Thermal pollution. 

Available from the National Technical Information Service, 

Springfield, Va. 22161 

PRICE CODE: PC A18/MF A01 

i 

7 
' ACKNOWLEDGMENTS 

We thank Drs. G. F. Cada and C. C. Coutant (ORNL), Paul Rag0 

(National Power Plant Team), Marcia Mulkey (NRC), and Barry Cohen, Tom 

Fikslin, Henry Gluckstern, and Jonathan Strong (EPA Region V) for their 

careful review of the testimony published in this report. 

.- . _-__. . . . . iii 

Preceding page blank 
FOREWORD 

On December 19, 1980, with the signing of an out-of-court 

settlement agreement, a three-year adjudicatory hearing on the effects 

of electric power generation on the Hudson River was ended. The 

purpose of this hearing had been to determine whether six cooling 

towers, required by the various Environmental Protection Agency (�PA) 

permits, should be built at three power plants on the Hudson River in 

New York in order to mitigate the impacts of entrainment and 

impingement on estuarine fish populations. In addition to terminating 

the EPA hearings, the settlement resolved regulatory disputes between 

the utility companies and several other federal agencies, including the 

U.S. Nuclear Regulatory Commission (NRC). 

Staff of the Environmental Sciences Division at Oak Ridge National 

Laboratory (ORNL) were asked to participate in the EPA hearings because 

of previous work on entrainment and impingement performed for NRC in 

connection with the licensing of Indian Point Units 2 and 3, the 

largest generating units on the Hudson River. ORNL Staff prepared and 

submitted, in May 1979, numerous individual pieces of written direct 

testimony for EPA as part of these hearings. Some of these pieces of 

testimony were coauthored with individuals from the National Power 

Plant Team of the U. S. Fish and Wildlife Service and from EPA. The 

purpose of this three-volume report is to publish these individual 

pieces of testimony involving ORNL staff in a manner that will assure a 

broader distribution to the scientific community, government agencies, 

and other interested parties. - Volume I is concerned with the estimation of the direct (or 

annual) entrainment impact of the power plants on populations of 

striped bass, white perch, Alosa spp. (blueback herring and alewife), 

American shad, Atlantic tomcod, and bay anchovy in the Hudson River. 

Entrainment impact results from the killing of fish eggs, larvae, and 

young juveniles that are contained in the cooling water cycled through 

a power plant. An "Empirical Transport Model" is presented as the 

means of obtaining a conditional entrainment mortality rate (which 

represents the fraction of a year class which would be killed due to 

V

Precedingpage-blank­ . -
vi 

entrainment in the absence of density-dependent mortality). Most of ' 

Volume I is concerned with the estimation of several parameters 

required by the model: physical input parameters (e.g., power-plant 

withdrawal flow rates); the longitudinal distribution of 

ichthyoplankton in time and space; the duration of susceptibility of 

the vulnerable organisms ; the "W-factors ,"which express the ratios of 

densities of organisms in power plant intakes to densities in the 

river; and the entrainment mortality factors, which express the 

probability that an organism will be killed if it is entrained. Once 

these values are obtained, the model is used to estimate entrainment 

impact for both historical conditions and projected conditions. 

Volume I1 contains four exhibits relating to impingement impacts 

and three critiques of certain aspects of the utilities' case. The 

first exhibit is a quantitative evaluation of four sources of bias 

(collection efficiency, reimpingement, impingement on inoperative 

screens, and impingement survival) affecting estimates of the number of 

fish killed at Hudson River power plants. The two following exhibits 

contain, respectively, a detailed assessment of the impact of 

impingement on the Hudson River white perch population and estimates of 

conditional impingement mortality rates for seven Hudson River fish 

populations. The fourth exhibit is an evaluation of the engineering 

feasibility and potential biological effectiveness of several types of 

modified intake structures proposed as alternatives to cooling towers 

for reducing impingement impacts. The reuiainder of Volume 11 consists 

of critical evaluations of the utilities' empirical evidence for the 

existence of density-dependent growth in young-of-the-year striped bass 

and white perch, of their estimate of the age-composition of the 

striped bass spawning stock in the Hudson River, and of their use of 

the Lawler, Matusky, and Skelly (LMS) Real-Time Life Cycle Model to 

estimate the impact of entrainment and impingement on the Hudson River 

striped bass population. 

Volume 111 addresses the validity of the utilities' use of the . -

Ricker stock-recruitment model to extrapolate the combined 

entrainment-impingement losses of young fish to reductions in the 

equilibrium population size of adult fish. In our testimony, a 

I , 
vii 

methodology was developed and applied to address a single fundamental 

question: if the Ricker model really did apply to the Hudson River 

striped bass population, could the utilities' estimates, based on 

curve-fitting, of the parameter alpha (which controls the impact) be 

considered reliable? The present Volume 111 includes, in addition, an 

analysis of the efficacy of an alternative means of estimating alpha, 

termed the technique of prior estimation of beta (used by the utilities 

in a report prepared for regulatory hearings on the Cornwall Pumped 

Storage Project). Our validation methodology should also be useful in 

evaluating inferences drawn in the literature from fits of 

stock-recruitment models to data obtained from other fish stocks. 
SUMMARY 

The first step in assessing how losses due to entrainrnent 

mortality may affect the future well-being of a fish population is 

calculating the conditional entrainment mortality imposed by power 

plants on that population. The conditional entrainment mortality rate 

is defined as the fraction of a population which would be killed due to 

entrainment in the absence of any other source of mortality. In this 

exhibit, historical and projected conditional entrainment mortality 

rates are estimated for six fish populations inhabiting the Hudson 

River estuary: striped bass, white perch, -Alosa spp. (blueback herring

and alewife), American shad, Atlantic tomcod, and bay anchovy. 

Chapter I, entitled "Mathematical Methods Used in Estimating 

Conditional Entrainment Mortality Rates of Six Hudson River Fish 

Populations," presents the Empirical Transport Model (ETM),which is 

the mathematical method used in this exhibit. Appendix A, which is 

available under separate cover, describes the derivation of the ETM in 

greater detail and provides a hypothetical example of how the model 

operates.. 

Chapter I1 of this exhibit is entitled "Physical Input Parameter 

Values Used to Estimate Conditional Entrainment Mortality Rates for Six 

Rudson River Fish Populations." This chapter presents the physical 

input parameter values used in the ETM. The physical parameters are 

the river region volumes, historical and projected power plant 

withdrawal flow rates, and the proportion of power plant water 

withdrawn frcm each river region. These values are used for ETM 

analyses of all six fish populations. 

Chapter I11 is entitled "Spatial Distributions of Entrainable Life 

Stages of Six Hudson River Fish Populations." These distributions are 

used as input to the ETM and are based on 1974 and 1975 Texas 

Instruments Inc. (TI) field data. 

Chapter IV, entitled "Durations of the Entrainment Intervals and 

Entrainment Periods for Six Hudson River Fish Populations," presents 

values used in the ETM for the total period of time individual members 

xi Preceding page blank 
xii 

of a given population take to grow through entrainable life stages 

(entraiment interval) and the range of calendar dates that entrainable 

individuals are present in the Hudson River (entrainment period). 

Chapter V is entitled "W-Factors for Hudson River lchthyoplankton 

Entrained at Bowline, Lovett, Indian Point, Roseton, and Danskammer." 

This chapter presents two alternatsve methods of calculating the 

W-factors (ratios of power plant intake organism densities to river 

densities) used in the ETM- Appendix B, which accompanies this 

chapter, discusses the sources of data used to compute U-factors using 

the two methods. 

Chapter VI, entitled "Estimating the Ratio of Power Plant Intake 

Organism Densities to River Densities Using the River Data 

Methodology," presents a third approach to estimating W-factors. 

approach was used when lack of sufficient data prohibited use of either 

This 

of the two approaches in Chapter V. 

Chapter VI1 is entitled "Entrainment Mortality Factors for Hudson 

River Lchthyoplankton at Bowline Point, Lovett, Indian Point, Roseton, 

and Danskammer Point Power Plants." Appendices C-F accompany this 

chapter and provide the sources of data and mathematical justifications 

for the entrainment mortality factors (f-factors) as they are used in 

the ETM. Appendix P, which also relates to this topic, summarizes 

indirect or sublethal effects and describes a method of incorporating 

such effects into entrainment mortality factor calculations. 

Chapter VI11 presents the results of the ETM runs using the input 

parameter values described in Chapters 11-VII. Appendices G-0, which 

accompany this chapter, list detailed ETM run results for each of the 

six fish populations. 

r.7 
ABSTRACT 

BOREMAN, J., L. W. BARNTHOUSE, D. S. VAUGHAN, C. P. GOODYEAR, 

S. W. CHRISTENSEN, R. D. KUMAR, B. L. KIRK, and 

W. VAN WINKLE. 1981. Entrainment impact estimates for 

six fish populations inhabiting the Hudson River 

Estuary. Volume I. The Impact of Entrainment and 

Impingement on Fish Populations in the Hudson River 

Estuary. ORNL/NUREG/TM-385/Vl and NUREG/CR-2220. 

Oak Ridge National Laboratory, Oak Ridge, Tennessee. 

This volume is concerned with the estimation of the direct (or 

annual) entrainment impact of power plants on populations of striped 

bass , white perch, -Alosa spp. (blueback herring and alewife), American 

shad, Atlantic tomcod, and bay anchavy in the Hudson River estuary. 

Entrainment impact results from the killing of fish eggs, larvae, and 

young juveniles that are contained in the cooling water cycled through 

a power plant. An Empirical Transport Model (ETM) is presented as the 

means of estimating a conditional entrainment mortality rate (defined 

as the fraction of a year class which would be killed due to 

entrainment in the absence of any other source of mortality). 

Most of this volume is concerned with the estimation of several 

parameters required by the ETM: physical input parameters (e.g., 

power-plant withdrawal flow rates); the longitudinal distribution of 

ichthyoplankton in time and space; the duration of susceptibility of 

&he vulnerable organisms; the W-factors, which express the ratios of 

densities of organisms in power plant intakes to densities of organisms 

in the river; and the entrainment mortality factors (f-factors), which 

express the probability that an organism will be killed if it is 

entrained. Once these values are obtained, the ETM is used to estimate 

entrainment impact for both historical and projected conditions. 

ix 

Preceding page -.. blank -
TABLE OF CONTENTS 

Chapter Page 

I Mathematical Methods Used in Estimating Conditional 

Entrainment Mortality Rates of Six Rudson River 

Fish Populations (J. Boreman, C. P. Goodyear, and 

S. W. Christensen) ................... x-1 

11 Physical Input Parameter Values Used to Estimate 

Conditional Entrainment Mortality Rates for Six 

Hudson River Fish Populations (J. Boreman) . . . 11-1 

111 Spatial Distributions of Entrainable Life Stages of 

Six Hudson River Fish Populations (J. Boreman) . 111-1 

IV Durations of the Entrainment Intervals and 

Entrainment Periods for Six Hudson River Fish 

Populations (J. Boreman) ................ IV-1 

V W-factors for Hudson River Ichthyoplankton 

Entrained.at Bowline, Lovett, Indian Point, 

Roseton, and Danskammer (L. W. Barnthouse, 

K. D. Kumar, and B. L. Kirk) .............. v-1 

VI Estimating Ratios of Power Plant Intake Organism 

Densities to River Densities Using the River'Data 

Methodology (J. Boreman) ................ VI-1 

VII Entrsinment Mortality Factors for Hudson River 

Ichthyoplankton at Bowline Point, Lovett, Indian 

Point, Roseton, and Danskammer Point Power Plants 

(D. S. Vaughan) .................... VII-1 

VI11 Conditional Entrainment Mortality Rate Estimates 

for Six Hudson River Fish Populations (J. Boreman 

and C. P. Goodyear .................. VIII-1 

APPENDICES 

A An Empirical-Transport Model*For Evaluating Entrainment 

of Aquatic Organisms by Power Plants. FWS/OBS-78/90. 

(3. Boreman, C. P. Goodyear, and S. W. Christensen) 

Under Separate Cover 

B Sources of Data Used to Computer W-factors- Using the 

MU and GBC Methods (L. W. Barnthouse, K. D. Kumar, and 

8. L. Kirk) ...................... B-1 

C 1. Derivations of Component f-factors 

2. Development of the Compromise f-factor 

(D. S. Vaughan) .................. c-1 

xiii 
xiv 

Chapter 

D 

E 

F 

G-L 

M-0 

P 

Detectability and Precision of f-?actor Estimates 

(D. S. Vaughan) .................... Additional Biases in Estimates of the f-factor 

(Do S.Vaughan) .................... Thermal Model f-factor for Four Hudson River 

Power Plants (D. S.. Vaughan) .............. ETH Run Results Incorporating Pooled f-factors 

(J. Boreman) ...................... ETM Run Results Incorporating Thermal Model 

f-factors (J. Boreman) ................. Incorporation of Sublethal Effects and Indirect . 

Mortality in Modeling Population-level Impacts of 

Power-plant Entrainment (W. Van Winkle and 

S. W. Christensen) ................... 

Page 

D-1 

E-l 

F-1 

r -L G-1 

/, 

M-1 

P-1 

.-­

,--
CHAPTER I 

MATHEMATICAL METHOIDS USED IN ESTIMATING 

CONDITIONAL ENTRAINMENT MORTALITY 

RATES OF SIX HUDSON RIVER FISH POPULATIONS 

Joint Testhny of 

30hn Boreman, Ph.D. 

C. Phillip Goodyear, Ph.D. 

National Power Plant Team 

U.S. Fish and Wildlife Service 

2929 Plymouth Road 

Ann Arbor, Michigan 48105 

and 

Sigurd W. Christensen, Ph.D. 

Environmental Sciences Division 

Oak Ridge National Laboratory

Oak Ridge, Tennessee 37830 + 

PREPARE3 FOR THE 

U.S. ENVXR0"SAL PROTECTION AGENCY, REGION I1 

May 1979 
1-1 

1. INTRODUCTION 

Ma hema ical me hods are used in this testimony for estimat-ng the 

conditional entrainment mortality rates imposed by Hudson River power 

plants on six fish populations (striped bass, white perch, blueback 

herring/alewife, American shad, Atlantic tomcod, and bay anchovy). 

Conditional entrainment mortality rates are estimated by using the 

Empirical Transport Model (Em)developed by Boreman et al. (1978). 

This reference, denoted herein as Appendix A, is available under 

separate cover (see *'References Cited" section); it provides more 

complete detail on the mathematical basis and application of the 

model. The purpose of this chapter is to present the mathematical 

formulation of the ETM, as used in this exhibit, discuss the physical 

and biological input parameters that are necessary to use the model, 

assess several assumptions implicit in the model, and compare the ETM 

to the models used by the utilities to estimate conditional entrainment 

mortality rates. 

2. MATHEMATICAL BASIS OF THE ETM 

The ETM can be used to estimate the conditional entrainment 

mortality rate of fish populations inhabiting the Hudson River. The 

ETM, which is essentially a mathematical equation, relies on knowledge 

of the morphometry of the water body; power plant water withdrawal flQw 

rates; the probability that an entrained organism will survive plant 

passage; and the duration, distribution, and relative abundance of 

entrainable life stages. A distinguishing feature of the ETM is that 

the distribution and movement of entrainable organisms among regions of 

the water body is defined by information derived from field samples. 

By using such information, the ETM avoids the difficulties often 

associated with hydrodynamic transport _-_ models, such as the Real-Time ~ __.____ 

Life Cycle Model (RTLCM) developed by Lawler, Matusky and Skelly - ­ _ _\ 

/

Engineers (LMS 1975), which rely on hydrodynamic principles and 

equations to define organism movement. Swartzman et al. (1978) discuss 
-- 

1-2 

problems associated with applying hydrodynamic models to biological 

data in order to assess power plant impacts. 

The ETM formulation used in this testimony is a "Type 11" ETM 

(p. 11, Boreman et al. 1978), which is applicable when time-dependent 

spatial distribution data are not available for each age group of the 

selected fish population. This condition prevails when distribution 

data are categorized by life stage rather than calendar age. As such, 

the observed spatial distributions over the entire entrainment period 

(the period of time during which entrainable life stages are present in 

the water body) are averaged over all weeks for each life stage, with 

each week's contribution weighted by its relative abundance. The 

resultant parameter values can be used to calculate the conditional 

entrainment mortality rate as follows (p. 13, Boreman et al. 1978): 

where 5 = total conditional entrainment mortality rate, 

s = week of the spawning period, 

S = total number of weeks in the spawning period, 

R = proportion of total eggs spawned that are spawned in 

S 

week s, 

j = age 0,1,2,...,3 (in weeks), 

J = oldest entrainable age, also the duration of the 

entrainment interval (in weeks), 

.t = life stage I.,2,3,...,Ls 

L = total number of entrainable life stages, 

k = region 1,2,...,K, 

K = total number of regions within the water body, 

Dke= average proportion of the total standing crop of life 

stage L individuals in region k during the entrainment 

period, 

I--

., 

~ 
- 

1-3 

Es+j,kC instantaneous entrainment mortality rate constant of 

life stage &individuals during week s+j in region k 

(units: per day), 

proportion of age j individuals in life stage C, and 

t= duration of the model time step (7 days). 

Calculation of the instantaneous entrainment mortality rate (E) 

requires specifying power plant flow rates, region volumes, and 

susceptibility of individuals within a region to withdrawal by power 

plants and subsequent mortality due to plant passage. These parameters 

are expressed mathematically as follows: 

- 's+j,k fs+j,ka 's+j,ka 

Es+j,u 'k 

where P 

s+j ,k = power plant water withdrawal flow rate from region 

k during week s+j (units: per day), 

*s+j ,ke = fraction of life stage a. individuals entering the 

intake that are eventually killed by plant passage 

occurring during week s+j in region k, 

W

s+j,k& = ratio of the average intake concentration to 

average region concentration of life stage t 

individuals during week s+j in region k, and 

Vk = volume of region k (assumed constant throughout the 

entrainment period). 

Figure 1-1 is a schematic diagram that illustrates operation of the ETM. 

Physical data necessary for the ETM are the number of geographic 

regions within the water body and their respective volumes, as well as 

the power plant withdrawal flow rates from each region. Necessary 

bilogical data include the distribution of entrainable organisms, by 

age or life stage, among the water body regions; the ratio of average 

intake to average regional concentrations of organisms (entrainment 

susceptibility factor, herein called the U-factor); the fraction of 

organisms entering the intake that is eventually killed by plant 
1-4 

E 

0 

a W 

a 

rl

I 

H 

al 

&

3 

M

rl' 

cr 
1-5 

passage (herein called the f-factor); and the'temporal distribution of 

egg deposition or recruitment to the first vulnerable life stage. In 

addition, selection of entrainment periods and entrainment intervals 

for each population is necessary. Subsequent chapters of this 

testimony (Chapters 11-VII) present more detailed definitions of each 

input parameter variable used in the ETM, as well as derivations of 

population-specific input parameter values. 

3. ASSUMPTIONS UNDERLYING THE ETM 

Several assumptions are implicit in the ETM. Violation of these 

assumptions may significantly reduce the accuracy of the estimate of 

the conditional entrainment mortality rate. Often the direction of the 

bias (whether the estimate is an overestimate or underestimate of the 

true conditional entrainment mortality rate) is known, but the degree 

of bias is not. 

The first assumption, and probably the most critical one, is that 

the data used to establish spatial and temporal distributions are 

accurate- Problems associated with gear bias, species and life stage 

identification, sample design, and data interpretation reduce overall 

accuracy of the ETM estimate. However, such problems also reduce 

accuracy in estimates from any other methodology that incorporates 

distribution and vulnerability data derived from field samples, such as 

the RTLCM and empirical methods used by the utilities in exhibits UT-3, 

UT-4, UT-6, and UT-7. 

A second assumption that is implicit in application of the ETM is 

that organisms move instantaneously among regions of the water body 

between time steps and do not move among regions within each time 

step. As such, near-field (i.e., within-region) depletion of organisms 

due to entrainment mortality within one region is not offset by 

movement of other organisms into the depleted region during a given 

time step. If the organism distribution data are collected during 

power plant operation, the data base for both the river-wide 

distribution and the W-factors inherently reflects the near-field 

depletion and the degree to which it is offset by organism movement 

during the time step. 
1-6 

If power plant effects are large enough in relation to organism . 

movement during the period of data collection to have substantially 

altered the distribution patterns of the organisms (i.e., through 

localized reduction in standing crops), the ETM estimates will be 

biased low. In addition, if the ETM is used to estimate conditional 

mortality rates for projected power plant flow conditions that are 

different from those conditions corresponding to the period of data 

collection, further biases can be expected. The direction of these 

further biases will depend on many factors and can be controlled, to 

some extent,'by making judicious choices for region size and length of 

the time intervals used in the model. 

Another salient aspect of the ETM methodology is the assumption 

that the organism distribution parameter values are based on field 

measurements of the entire standing crop of each entrainable life 

stage. If some members of an entrainable life stage are located 

outside the area of sampling, then the ETM will overestimate the 

conditional entrainment mortality rate for the entire population. 

Nevertheless, it will still reflect the mortality rate of that portion 

of the population that remains within the sampled area. 

A final assumption concerns.the uniformity of natural mortality 

within the modeled system. The "Type 11" ETM, as used in this 

testimony, implicitly assumes that the natural mortality rate of a 

given life stage is the same in all regions of the water body during 

the entire time that life stage is present within the entrainment 

period; that is, no differential natural mortality occurs among regions 

of the water body. If differential natural mortality does occur, and 

it is measureable, then a more generalized version of the ETM (Eq. 5, 

Boreman et al. 1978) can be utilized to calculate the conditional 

, ,I entrainment mortality rate. However, time- and age-dependent natural 

mortality rates dueing the entrainment periods for the six fish 

populations were not measured by the utilities' consultants or computed 

by US* 

The "Type 11" ETM also assumes that natural mortality rates are 

independent of population density. If density-dependent mechanisms 

such as cannibalism, or density-dependent forms of starvation or 
1-7 

predation are operative concurrently with reduction in numbers due to 

entrainment mortality, this assumption will not be realistic. 

Density-dependent mortality can be incorporated directly into ETM 

estimates by allowing the natural mortality rate during a given time 

step to be a function of the population size at the beginning of that 

time step, or incorporated indirectly by multiplying the final ETM 

estimate by a coefficient that accounts for compensatory capability of 

the population during the entrainment period. The former technique 

involves quantification of the density-dependent mortality function in 

space and time, which is generally beyond the present state-of-the-art 

and has not been undertaken for tiudson River fish stocks. The latter 

technique requires a prolonged series of appropriate stock and 

recruitment data (Christensen et al. 1977), which are not available for 

the six Hudson River fish populations. Since both techniques are 

extremely difficult to quantify even approximately, the level of 

confidence in the estimates would almost always be very low. An 

alternative to using either of these techniques when density-dependent 

mechanisms are known or assumed to be operative during the entrainment 

period.is to make a judgment of the significance of the effect based on 

the conditional entrainment mortality rate estimate from the ETM. 1 

4. COMPARISON OF THE ETH TO THE UTILITIES' EMPIRICAL MODELS 

In their direct testimony, the utilities presented two empirical 

methodologies for calculating the conditional entrainment mortality 

rates of selected fish populations inhabiting the Hudson River. One 

methodology, developed by Texas Instruments, Inc. (TI) (p. 2-VI-3, 

Exhibit UT-3), compares the estimated number of each entrainable life 

stage "cropped" by entrainment during a specified time interval to the 

standing crop of ichthyoplankton present in the river ("adjusted" to 

account for incomplete recruitment of eggs to the entrainable 

population) during the same speciefied time interval. The second 

methodology, developed by LMS (pp. 9.1B-5 to 9.1B-10, exhibits UT-6 and 

UT-7) divides the total number of organisms entrained by a power plant 
1-8 

by the average standing crop of entrainable organisms during the 

entrainment period. 

4.1 TI METHODOLOGY 

The TI methdology adjusts for the fact that, before all eggs are 

spawned, the entrainment mortality imposed by power plants is only 

experienced by that part of the population present in the river and not 

the entire population of entrainable organisms that will be present 

after all the eggs are spawned. This adjustment for unspawned 

individuals, however, is not complemented by a necessary similar 

adjustment at the end of the entrainment period which would allow 

recruitment to the first non-entrainable life stage. The failure to 

use this complementary adjustment could result in an overestimate of 

the conditional entrainment mortality rate. As shown in subsequent 

chapters of this exhibit, however, when the TI methodology is applied, 

entrainment of all selected species is arbitrarily cut off before 

recruitment to the first non-vulnerable life stage is fully realized. 

Because of this, entrainment mortality is not accounted for at all 

after the cutoff date. Whether this approach overestimates or 

underestimates the conditional entrainment mortality rate will depend 

on the particular cutoff date used. The ETM, by using a cohort 

approach, allows for an explicit temporal distribution of recruitment 

to the first entrainable life stage similar to that used in the TI 

methodology, as well as an explicit temporal distribution of 

recruitment to the first non-vulnerable life stage, which the TI 

methodology lacks. 

An assumption underlying the TI methodology is that, within each 

time interval during the entrainment period, the various entrainable 

life stages of a given species present in the water body during that 

time interval have exactly the same distribution patterns (Tr. 10340). 

In calculating their entrainment ratio (ratio of the number of 

organisms entrained to the standing crop in the river), TI combines 

organisms across life stages in both the nirmerator and the 

denominator. Thus, in any given week, It is the loss of the most 

I -

,” 
1-9 

numerically abundant organisms which will govern the size of the 

ratio. The fact that the older, and hence less abundant organisms 

which are killed are individually more valuable to the population is 

not properly accounted for in an equation as simplistic as equation 

2-VI-2 of Exhibit UT-3, unless all organisms have the same distribution 

regardless of age, and power plant impacts are spread evenly among the 

different ages. 

The weakness of TI's approach is best illustrated with a simple 

example involving a hypothetical population. Suppose spawning occurred 

evenly over a four-week period with one billion eggs spawned each week, 

and there are three entralnable life stages (egg, larva, and juvenile) 

in the population, each lasting one week with a 90 percent mortality 

between each life stage. In the fourth week suppose that 60 percent of 

the juveniles were in a power plant region and the power plant killed 

20 percent of all individuals in that region. Simple arithmetic shows 

that the loss of juveniles alone to this power plant represents a 

conditional mortality rate of 0.03 to the entire year-class. This 

number is derived by considering that, since spawning was evenly 

distributed through four weeks, one-fourth of the potential year-class 

exists as juveniles in week 4, and 60 percent of these are within the 

power plant region. 

theref ore : 

Of this 60 percent, 20 percent are killed, 

(0.25)(0.60)(0.20) = 0.03, 

or 3 percent of the year-class is destroyed as juveniles in this week. 

Assume, however, that the power plant is located downstream, while 

most spawning occurs upstream and only 10 percent of the eggs are found 

in the power plant region. For the sake of simplicity, also assume 

that no larvae are killed by the plant. Now it is possible to evaluate 

what TI's equation would calculate for that week. 

Ten percent of the one billion eggs are located within the.power. 

plant region; 20 percent of these, or 20 million eggs, are killed. 

This represents an additional 0.5 percent loss to the total potential 
1-10 

year-class (0.25 x 0.10 x 0.20 = 0.005) and, between eggs and 

juveniles, 3.5 percent of the year-class has been killed. With 90 

percent mortality between eggs and larvae and between larvae and 

juveniles, there will be 10 million juveniles in this week (week 4), 60 

percent (6 million) are in the power plant region, and 20 percent of 

these (1.2 million) are killed. 

The numerator of TI's entrainment ratio is the sum of the eggs and 

juveniles that are killed: 20 million eggs plus 1.2 million juveniles 

equals 21.2 million organisms. The denominator of TI's ratio is the 

total standing crop of organisms in the system: one billion eggs plus 

100 million larvae plus 10 million juveniles, or 1.11 x 109 

organisms. The ratio of these sums is 0.019. This ratio would form 

TI'S estimate of the quantity mi in their equation 2-VI-2 in Exhibit 

UT-3, which is defined as the "probability of death from entrainment 

during the ith interval." If the power plant operated only during 

that one week ' mi would also be equal to TI's quantity 3in 

equation 2-VI-1 of Exhibit UT-3, defined as the "conditional mortality 

rate due to entrainment." 

Clearly, the estimate of 0.019 is a substantial underestimate of 

the true impact during that week (0.035). The cause of this difference 

is due to the different spatial distributions of eggs and juveniles. 

Although the juveniles are a more importanf component of the actual 

loss because each juvenile has a much higher survival value than each 

egg, their loss is masked by the loss of the more abundant eggs. The 

estimate of entrainment is thus governed disproportionately by the 

distribution of eggs. 

The proper "conditional mortality rate due to entrainment" cannot, 

in general, be obtained from equations 2-VI-1 and 2-VI-2 in Exhibit 

UT-3. The desired conditional probability of entrainment mortality is 

defined as: 

Ne

mTir (1-3). 

0 
1-11 

where 9= conditional mortality rate due to entrainment, 

N 5 the number of non-entrainable fish produced with the 

e 

power plant operating, and 

N = the number of non-entrainable fish produced without the 

0 

power plant operating. 

In a situation where the probability of entrainment mortality 

differs for organisms of different ages, either due to different 

spatial distributions or different vulnerabilities to the power plant, 

Equation 2-VI-1 in Exhibit UT-3 will only be correct if the individual 

mi components in Equation 2-VI-2 in Exhibit UT-3 have been calculated 

with proper weighting for the various life stages. That entire concept 

is missing from TI'S "Empirical Entrainment Methodology", but not from 

the ETM. 

The ETM addresses this concept by handling the distribution 

pattern and entrainment susceptibility of each life stage separatelyThe ETM approach is more realistic for two reasons: (1) life stages 

may have different vertical and longitudinal distribution 

characteristics, as well as differential susceptibility to power plant 

induced mortality, and (2) the total standing crops of the entrainable 

life stages that are present concurrently in the water body may vary by 

as much as several orders of magnitude. 

4.2 LMS METHODOLOGY 

Since intake samples are used, the LMS methodology implicitly 

assumes that sampling efficiencies of gear used at power plant intakes 

are equal to efficiencies of gear used to measure rivervide densities 

of organisms. .Carpenter (1979) and Barnthouse et al. (Chapter V) show 

that the intake sample densities are biased when compared to densities 

obtained by river sampling and that direct comparisons reflect this 

bias. Consequently, the conditional entrainment mortality rates 

derived by the LMS methodology are biased estimates of the true 

values. The extent to which the true values are underestimated or 

overestimated depends on the degree of differential sampling 

efficiencies among the gear.used at the power plant intakes and in the 

river. 
^. 

1-12 

4.3 ADDITIONAL CONSIDERATIONS 

Additional differences among the ETM and utilities’ estimates of 

conditional entrainment mortality rates are due to different values 

used for the input parameters in the various methodologies. 

Specifically, differences exist for power plant withdrawal flow rates 

(actual and projected) , duration of the entrainment periods , the 

fractions of organisms entering the power plant intakes that are killed 

by plant passage, and the relationships between intake densities and 

near-field densities. These differences are discussed as each input 

parameter value used in the ETN analyses is introduced. 
1-13 

REFERENCES CITED 

Boreman, J., C. ?= Goodyear, and S. W. Christensen. 1978. An empirical 

transport model for evaluating entrainment of aquatic organisms by 

power plants. U.S. Fish and Wildlife Serv. FWS/OBS-78/90. 67 pp. 

Christensen, S. W., D. L. DeAngelis, and A. G. Clark. 1977. 

Development of a stock-progeny model for assessing power plant 

effects on fish populations. pp. 196-226. IN W. Van Winkle 

(ed.), Assessing the Effects of Power-Plant-Induced Mortality on 

Fish Population. Pergamon Press, New York. 380 pp. 

Exhibit EPA-196. Carpenter, E. J. 1979. Sampling ichthyoplankton. 

Exhibit UT-3. Supplement I to influence of Indian Point Unit 2 and 

other steam electric generating plants on the Rudson River 

estuary, with emphasis on striped bass and other fish 

populations. Edited by J. T. McFadden and J. P. Lawler. 

Submitted to Consolidated Edison Company of New York, Inc. July 

1977. 

Exhibit UT-4. Influence of Indian Point Unit 2 and other steam 

electric generating plants on the Hudson River estuary, with 

emphasis on the striped bass and other fish populations. Edited 

by J. T. McFadden. Prepared for Consolidated Edison Company of 

New York, Inc. July 1977. 

Exhibit UT-6. Roseton Generating Station. Near-field effects of oncethrough cooling system operation on Hudson River biota. Central 

Hudson Gas and Electric Corporation. July 1977. 

Exhibit UT-7. Bowline Point Generating Station. Near-field effcts of 

once-through cooling system operation on Hudson River biota. 

Orange and Rockland Utilities, Inc. July 1977. 

Lawler, Matusky and Skelly, Engineers (LMS). 1975. Report on 

development of a real-time two dimensional model of the Hudson 

River striped bass population. Prepared under contract with 

Consolidated Edison Company of New York, Inc. 

Swartzman, G. L., R. B. Deriso, and C. Cowan. 1978. Comparison of 

simulation models used in assessing the effects of 

power-plant-induced mortality on fish populations. U. S. Nuclear 

Regulatory Commission. NUREG/CR-0474. 155 pp. 
CHAPTER 11 

PHYSICAL INPUT PARAMETER VALUES USED TO 

ESTIMATE CONDITIONAL ENTRAINMENT MORTALITY 

RATES FOR SIX HUDSON RIVER FISH POPULATIONS 

Testimony of 

John Boreman, Ph.D. 

~ 

National Power Plant Team 

U.S. Fish and Wildlife Service 

2929 Plymouth Road 

Ann Arbor, Michigan 48105 

PREPARED FOR THE 

U.S. ENVIRONMENTAL PROTECTION AGENCY, REGION I1 

May 1979 
1. INTRODUCTION 

Use of the Empirical Transport Model (ETM) for estimating the 

conditional entrainment mortality rate of fish populations requires 

selection of appropriate biological and physical input parameter values. 

Although the biological input parameters vary depending on the particular 

fish population being examined, the physical input parameter values used 

in the ETM are the same for all the populations. The physical input 

parameter values used in the ETM to estimate conditional entrainment 

mortality rates include water volumes of each region, power plant water 

withdrawal flow rates, and distribution of the power plant water withdrawal flow rates among regions. 

2. REGION VOLUMES 

Region volumes used in the E& are listed in Table 11-1. The 

regions were chosen to coincide with the stratified sampling scheme 

employed by TI in its Long River, beach seine, fall shoals, and bottom 

trawl surveys conducted during 1974 and 1975. The regions were defined 

by TI on the basis of morphometric characteristics such as depth, width, 

and extent of shoals (areas 5 6m deep) (p. 6.3, Exhibit UT-4). 

The procedure TI used to estimate region volumes is presented in 

the First Multiplant Report (p. D-3, TI 1975). The volume of water in 

each of the three depth strata within each mile was calculated from the 

surface area and depths recorded on United States Geological Survey 

(USGS) maps of the river. Total surface area of each miie segment was 

obtained with the use of a polar planimeter. As such, volume estimates 

rely on accuracy of the USGS maps and the planimetric technique used on 

the maps. In addition, the shoreline used in the USGS maps represents 

conditions at mean low water. Therefore, the depth soundings used to 

estimate strata volumes are 1-2 feet below mean tidal depths, as noted 

in the legend of the USGS maps for the Hudson River. Since average 

river depth varies among regions, estimated volumes of the regions with 

11-1 
-- 

11-2 

Table 11-1. Water Volumes for the 12 Longitudinal Regions of the 

Hudson River Estuarya 

Region River Miles Volume (m3) 

Yonkers (YK) 14-23 229,420,287 

Tappan Zee (TZ) 24-33 321,811,465 

Croton-Haverstraw (CH) 34-38 147,736,754 

Indian Point (SP) 39-46 208,336,266 

West Point (WP) 47-55 207,455,769 

Cornwall (CW) 56-61 139,791,019 

Poughkeepsie (PK) 62-76 298,133,444 

Hyde Park (HP) 77-85 165,484,666 

Kingston (KG) 86-93 141,469,879 

Saugerties (SG) 94-106 176,295,711 

Cat skill (CK) 107-124 160,731,743 

Albany (AL) 125-140 71,149,105 

asupplied to EPA by Marcellus (1978) 

_-

I 
11-3 

shallow depths will be more biased by tidal conditions than deeper 

regions. 

3. POWER PLANT WITHDRAWAL FLOW RATES 

Several sets of power plant water withdrawal flov rates are used in 

the ETM analyses: histcrical (1971 and 1975) and projected. The . . 

historical flow rates, based on daily rates supplied to EPA by the 

utilities (Euggins 1977; Hutchisou 1977; Marcellus 1977) , are listed in 

tables IX-2 and 11-3 for 1974 and 1975, respectively. The rates in 

these tables are expressed as average daily withdrawal flows for each 

week. 

Projected flows rates (Table 11-4) were obtained from several 

sources. The once-through and closed-cycle cooling water withdrawal 

rates for Bowline, Indian Point units 2 and 3, and Roseton are based on 

the lifetime average flow conditions listed in tables A-1 through 

A95 of Exhibit UT-3 and Marcellus (1979). Projected flov rates for 

Lovett units 4 and 5 and Danskarumer units 3'and 4 are the averages of 

1974 and 1975 values, as provided by the utilities (Huggins 1977, Hutchison 

1977; Marcellus 1977). Lovett units 103~Danskammer units 1and 2, and 

Indian Point Unit 1are not included in the projections because they are 

expected to be used very little, if at all, In the future (p. 5-180, 

Barnthouse et al. 1977). 

4. POWER PLANT FLOW RATE DISTRIBUTIONS AMONG REGIONS 

Due to the tidal nature of the Hudson River estuary, the power 

plants may withdraw water from more than one region. Each power plant's 

water withdrawal Is assumed to be directly related to the proportion of 

the tidal cycle when water "belonging" to a particular region is in 

front of the plant. In the Hudson River, the average tidal excursion 

distance is an estimated 13 miles (p. VI-12, TI 1975). The total Withdrawal 

flow for each power plant is, therefore, distributed among the regions 

in the ETM according to the propo-tion of the volume of the 13 mile 
11-4 

-

Table 11-2. Actual 1974 Water Withdrawal Flow Rates, Expressed as 

TCM!day, of Hudson River Power Plants Based on Average of 

Daily Flow Rates During Each Week -

Indian Pointb -

Interval Bowlinea Lovetta Unit 1 Unit 2 RosetonC DanskamerC 

4122-4/28 1697 1226 1036 1482 1219 1004 

4129-515 1722 1223 1234 3124 894 1307 

516-5112 1722 1223 710 2750 527 1131 

5I13-5119 1613 1223 947 1832 359 1209 

5120-5/26 0 1271 1638 3976 235 1341 

5127-612 0 1174 1621 4629 489 1335 

613-6/9 1898 1296 1638 4432 373 1134 

6110-6/16 2492 1180 1640 3768 334 1013 

6/17-6/23 3447 1333 1633 3914 349 1165 

6/24-6130 3155 1223 1414 4190 400 1145 

711-717 3236 1343 1511 4430 345 1368 

718-7I14 2822 1285 892 4433 380 1409 

7/15-7121 3445 1034 1646 3913 366 1477 

7122-7/28 3445 1222 1646 3666 1168 1654 

7/ 29-814 3445 1159 1646 1300 i203 1686 

8/5-8/11 3183 1291 1532 3599 2084 1629 

8/ 12-8118 3445 1350 1646 4510 1556 1527 

8/ 19-8I25 3639 1251 1646 4613 2238 1627 

8126-9I1 3565 1410 1646 4688 2279 1573 

912-918 3816 1233 1601 3839 1284 1513 

919-9/15 3816 1250 1646 4200 604 1624 ' 

9/16-9/22 4112 1219 1646 4090 2190 1302 

9123-9/29 4186 1301 1646 3927 2144 1325 

9/30-10/6 4186 778 1370 1617 2027 1648 

10/7-10/13 4186 696 1646 2009 1561 1688 

10/14-10120 4186 939 1646 3913 175 1616 

10/21-10127 369Q 998 1646 3913 0 1435 

10/27-1113 3567 939 1618 3609 537 1427 

asupplied to EPA by Hutchison (1977) 

bsupplied to EPA by Marcellus (1977) 

'supplied to EPA by Huggins (1977) 
11-5 

Table 11-3. Actual 1975 Water Withdrawal Flow Rates, Expressed as TCMIday,

of Hudson River Power Plants Based on Average of Daily Flow Rates 

During Each Week 

Indian Pointb 

Interval Bowlinea Lovetta Unit I Unit 2 Roseton' ~aiskam==~ 

312-318 2216 949 740 1272 2278 452 

319-3115 2802 839 740 752 2278 452 

3/16-3122 2802 758 740 791 2279 456 

3/23-3129 2802 039 740 983 2298 457 

3130-4 15 2802 821 740 1312 2279 452 

4 / 6-4 /12 2802 788 740 2375 2279 534 

4/13-4119 2802 684 704 2938 2296 452 

4120-4 I26 2802 655 953 3420 2279 454 

4 127-513 2986 655 33 3584 2565 789 

5/4-5/10 2165 711 588 3699 2712 840 

5/11-5117 1447 760 796 3913 2657 882 

5/18-5 I24 1401 1367 312 3915 3058 880 

5/25-5131 1401 1223 185 4 117 3004 1076 

611-617 2368 1223 446 4492 2953 1427 

6/8-6114 2802 1197 713 4582 2274 1481 

6115-6 I21 2802 1081 556 4714 2474 1551 

6/22-6128 2848 1278 450 4703 3058 1656 

6129-7 /5 2802 1060 187 4453 2824 1619 

716-7112 2802 1151 222 4643 2824 1292 

7/13-7/19 2802 1167 107 4688 2932 1424 

7120-7126 3353 1253 565 4659 3011 1487 

7127-812 3429 1359 867 1756 3058 1519 

813-819 3445 1250 705 117 2722 1634 

8110-8 I16 3445 1205 .32 4033 2125 1544 

8/17-8123 3362 1221 25 4390 2324 1547 

8124-8 / 30 3356 1216 270 4645 2279 1590 

8131-9/6 3445 1223 25 4715 2279 1494 

917-9113 3364 1235 25 3897 2279 1506 

9114-9 120 3445 634 25 3979 1928 1561 

9/21-9127 3445 589 25 3925 2283 1536 

9128-10 I4 3323 943 55 3770 2427 1484 

1015-10111 1723 1150 112 3925 2574 1177 

10112-10118 1723 941 112 2995 2528 1352 

10/19-10125 1565 955 111 872 2309 1548 

10126-11/1 1608 1076 112 1789 2279 1307 

"supplied to EPA by Hutchison (1977)

bsupplied to EPA by Marcellus (1977) 

'supplied to EPA by Huggins (1977) 
11-6 

Table 11-4. Projected Water Withdrawal Flow Rates, Expressed as 

TCM/day, of Hudson River Power Plants 

Indian Point' 

Interval . Bowlines Lovettb Units 2 li 3 , Rosetond Danskannnerb 

April 1543 2061 482 

(48) (78) 

hY 2524 1061 6470 2644 720 

(78) (434) (70) 

June 3816 1164 6442 3034 945 

(87) (439) (74) 

July 4186 1133 9130 3482 1062 

(87) (543) (87) 

August 4186 1176 9123 3482 1237 

(87) (545) (87) 

September 3657 1030 7919 2730 1179 

(83) (542) (38) 

October 2544 903 6971 1780 1084 

177) (484) (34 1 

afrom tables A-4 and A-5 of Exhibit UT-3 and Marcellus (1979) 

baverage of 1974 and 1975 values for units expected to be in operation 

cfrom tables A-2 and A-3 of Exhibit UT-3 and Marcellus (1979) 

dfrom Table A-1 of Exhibit UT-3 and Marcellus (1979) 

Numbers in parentheses are average monthly flows with closed-cycle 

cooling conditions ,..-
11-7 

segment surrounding each power plant intake that fails within each 

region. These proportions are listed in Table 11-5. 

5. REFERENCE CITED 

Barnthouse, L. W., J. B. Cannon, S. G. Christensen, A. H. Eraslan, 

J. L Harris, K. R. Kim, M. E. Laverne, H. A. McLain, B. D. Murphy,

R. 3. Raridon, T. H. Row, R. D. Sharp, and W. Van Winkle. 1977. 

A selective analysis of power plant operation.on the Hudson River 

with emphasis on the Bowline Point Generating Station. Oak Ridge 

National Laboratory. ORNL/TM-5877/V2. 

Exhibit UT-3. Supplement I to influence of Indian Point Unit 2 and 

other steam electric generating plants on the Hudson River 

estuary, with emphasis on striped bass and other fish 

populations. Edited by J. T. McFadden and J. P. Lawler. 

Submitted to Consolidated Edison Company of New York, Inc. 

July 1977-

Exhibit UT-4. Influence of Indian Point Unit 2 and other steam 

electric generating plants on the Hudson River estuary, with 

emphasis on striped bass and other fish populations. Edited by

J. To McFadden. Prepared for Consolidated Edison Company of New 

York, Inc. 1977. 

Huggins, To 1977 (pers. corn.). Letter to Henry Gluckstern (EPA) 

dated October 31, 1977. 

Hutchison, J. E. 1977 (pers. comm.). 

(FWS) dated October 27, 1977. 

Marcellus, K. L. 1977 (pers. corn.). 

dated November 10, 1977. 

Marcellus, KO I..1978 (pers. comm.). 

dated April 12, 1978. 

Marcellus, K. i. 1979 (pers. comm.). 

dated March 2, 1979. 

Letter to C. Phillip Goodyear 

Letter to Henry Gluckstern (EPA) 

Letter to Henry Gluckstern (EPA) 

Letter to Henry Gluckstern (EPA) 

Texas Instruments, Inc. (TI) 1975. First annual report for the multiplant impact study of the Hudson River Estuary. Vols. 1-11. 

Prepared under contract with Consolidated Edison Company of New 

York, Inc. 
11-8 

Table 11-5. Proportion of Power Plant Cooling Water Flows Withdrawn from 

each Longitudinal Region of the Hudson Rivera 

Power Plant 

Region Bowline Lovett Indian Point Roseton Danskammer _-

YK 

TZ 0.271 0 0 0 0 

CH 0.358 0.369 0.298 0 0 

IP 0.371 0.549 0.562 0 0 - -

WP 0 0.082 0.140 0 0 

__ cw 0 0 0 0.273 0.196 

PK 0 0 0 0.727 0.804 

HP 0 0 0 0 0 

KG 0 0 0 0 0 

SG 0 0 0 0 0 

CK 0 0 0 0 0 

AL 0 0 0 0 0 ,~ -

abased on a 13 mile daily tidal excursion and river volume data r­

(Table D-2, TI 1975) 
CHAPTER I11 

SPATIAL DISTRIBUTIONS OF ENTRAZNABLE 

LIFE STAGES OF SIX HUTSON RIVER 

FISH POPULATIONS 

Testimony of 

John Boreman, Ph.D. 

National Power Plant Team 

U.S. Fish and Wildlife Service 

2929 Plymouth Road 

Ann Arbor, Michigan 48105 

PREPARE3 FOR THE 

U.S. ENVIRONMENTAL PROTECTION AGENCY, REGION 11 

May 1979 
1 INTRODUCTION 

Susceptibility of an individual organism to withdrawal by power 

plants depends on the location of that individual within the water body 

in relation to the location of power plant intakes. This relationship 

may change'duringthe individual's entrainable life stages as the 

individual changes its position within the water body either by movement 

or passive transport. The Empirical Transport Model (ETM), used in this 

exhibit to estimate the conditional entrainment mortality rates of six 

populations inhabiting the Hudson River estuary, accounts for differential 

susceptibility of individuals due to their location within the water 

body and movement of these individuals as they age. The parameter in 

the ETM that accomplishes this is the D parameter (equation 1-1 in 

Chapter I of this exhibit). The D parameter is the proportion of the 

total standing crop of a life stage that is present within a specified 

region of the water body. 

The "Type 11" ETM used in this exhibit implicitly assumes that the 

distributions of each life stage may be different, but that the distribution 

of a given life stage remains constant throughout the period of occurrence 

of that life stage in the water body. To meet this assumption, the 

distribution of each life stage represents an average of the 

distributions of that life stage recorded during the period that it is 

present in the water body. The average is weighted by the relative 

abundance represented by each distribution to assure that the 

distributions of most members of a life stage influence the resultant 

average to the greatest extent. 

2. DERIVATION OF PARAMETER VALUES 

Egg and larval life stage distributions were derived from Texas 

Instruments, Inc. (TI) Long River Survey data. The TI sampling program 

was designed to sample ichthyoplankton in most of the Hudson River 

(RM 14-140). Life stage distributions of entrainable juveniles were 

derived from either the Long River Survey data or the Long River Survey 

111-1 
111-2 

data used in conjunction with TI beach seine data, depending on the 

lateral and vertical distributions of juveniles that are characteristic 

for the species of concern. 

Average regional density data for each entrainable life stage of 

the selected fish populations, based on 1974 and 1975 Long River surveys, were provided to EPA and its consultants by the utilities 

(Marcellus 1977, 1978, 1979). Only 1974 data were provided for the 

tiudson River population of American shad. A description of the 

methodology used by TI to calculate regional densities of each life 

stage of the selected populations is in the First Annual Multiplant 

Report (p.D-5, Vol. 11, TI 1975). 

The proportional distribution of a given life stage among regions 

was derived by multiplying the density of that life stage in each region 

during a specified time interval (sample week) by the respective region 

volume. The resultant weekly standing crops in each region were divided 

by the estimated weekly river-wide standing crop to derive the proportion of the estimated river-wide standing crop present in each region 

during that week. These proportions were then averaged across all weeks 

that life stage was present in Long River Survey collections, weighting 

by the river-wide standing crops estimated for the respective weeks. 

For all fish populations except the Atlantic tomcod, which is a 

demersal species (Boreman 1979), the spatial distributions of entrainable juveniles were derived by adding the average weekly standing crop 

in each region, as estimated by the Long River Survey, to the average 

weekly shorezone standing crop, as estimated by the beach seine survey. 

Incorporation of beach seine data into the calculation of relative 

distributions for the more pelagic populations allows recognition of 

organism movement into the shorezone areas of the estuary during the 

juvenile life stage. As stated in the testimony on life histories 

(Boreman 1979), the relatively few juvenile tomcod caught in beach 

seines implies little movement of this species into the shorezone 

during its entrainment period. 

,-. 

, . 
111-3 

The resultant regional standing crop sums were divided by the sum 

of the average river-wide standing crop plus the average total shorezone 

standing crops to derive the proportion of the entrainable juvenile life 

stage present within each region. The use of average weekly standing 

crops avoided the problems of non-comparability of data bases associated 

with the several non-overlapping sampling weeks of the two surveys. 

Beach seine data sometimes extended several weeks past the final week 

of Long River Survey data collection. 

Standing crops estimated with the beach seine data were derived by 

multiplying the average regional catch per tow during a specified sample 

week by the average area swept by a tow. This product was divided by 

the shorezone surface area of the specified region (Table D-3, Vol. IT,

TI 1975). The same method of calculating shore zone standing crops was 

used by the utilities' consultants (p. D-9, Val. 11, TI 1975). The 

resulting average regional shorezone standing crops were then averaged 

across sample weeks, weighted by the total shorezone standing crop 

present in the river during each respective sample week. 

Because of problems associated with gear avoidance, the standing 

.crops derived by the above methodologies should not be regarded as 

absolute estimates of the true regional or river-wide standing crops. 

Rather, as used in the ETM, they are considered as direct indices of the 

true standing crops, necessitating an assumption that sampling gear 

catch efficiencies are equal in all regions of the water body. Factors 

that will affect the validity of this assumption are, for the most part, 

abiotic. The factors include salinity, tidal-induced motion, water 

temperature, and water body morphometry (Table 2, Bowles et al. 1978). 

However, no data are available that relate the influence of such factors 

present in the Hudson River to sampling gear catch efficiencies. 

3. PARAMETER VALUES FOR SELECTED FISH POPULATIONS 

Average entrainable life stage distributions used in tk.? ETM to 

estimate the conditional entrainment mortality rates of selected fish 
111-4 

populations are listed in tables 111-1 to 111-6 for striped bass, white 

perch, Alosa spp. (blueback herring and alewife), American shad, Atlantic 

tomcod, and bay anchovy, respectively. Table III-7 lists the dates of 

the TI beach seine data collections used in estimating the distribution 

of entrainable juveniles of all populations except Atlantic tomcod. 

Only day beach seine samples were used because night samples did not 

encompass the entire estuary. 

r "I 

As stated previously, the relatively low catch of Atlantic tomcod 

in beach seines coupled with the epibenthic characteristics of the juvenilc 

life stage of this species precluded use of beach seine data in the 

derivation of the spatial distribution of that life stage. The time 

intervals of beach seine data collections used to estimate the distributioi 

of entrainable juveniles of the other fish populations were chosen to 

represent the period when that life stage was present in the Hudson River. 

This interval extended more than several weeks past the last date of 

Long River Survey data collection only for the bay anchovy population, 

which essentially remains in the entrainable size range until maturity. 

4. REFERENCES CITED 

I-" Boreman, J. 1979. Life histories of seven fish species that inhabit 

the Hudson River estuary. 94pp. Exhibit EPA -198. 

Bowles, R. R., J. V. Merriner, and J. Boreman. Factors affecting accuracy 

of ichthyoplanicton samples used in power plant-entrainment studies. 

U.S. Fish and Wildl. Serv. FWS/OBS-76/20.7. 12 pp. 

Marcellus, K. L. 1977 (pers. comm.). 

dated November 30, 1977. 

Marcellus, K. L. 1978 (pers. comm.). 

dated Jme 23, 1978. 

Marcellus, K. L. 1979 (pers. corn.). 

dated February 21, 1979. 

Texas Instruments, Inc. (TI). 1975. 

multiplant impact study of the Hudson River estuary. 

,-..

Letter to Henry Gluckstern (EPA) 

Letter to Henry Gluckstern (EPA) 

Letter to Henry Gluckstern (EPA) -_ 

First annual report for the 

Vols. 11. 

Prepared under contract with Consolidated Edison Company of New 

York, Inc. -7 
111-5 

Table 111-1. Proportional Distributions, Expressed as Percentages, of 

Entrainable Ltfe Stages of'Striped Bass as Used in the ETM Analyses 

Yolksac Post yolksac ' Entrainable 

Year Region Egga larvaa larvaa juvenileb 

10 07 

1974 YII 0 0.14 0.07 0.62 

TZ 0.09 2.65 3.84 28.41 

CH 18.20 6.72 23.10 

XP 23.97 11.43 23.87 5.01 

WP 36.54 10.39 21.71 2.72 

cw 2.63 22.12 12.28 11.38 

PK 4.12 35. 30 180 70 2.74 

HP 3.99 5.45 4.04 2.52 

KG 5.85 1.40 6.47 9.43 

SG 1.88 0.73 1.26 4.60 

CK 2.55 0.29 1.03 7.40 

At 0.18 0.'03 0.01 2.07 

1975 YK 0 0.05 0.51 2.05 

Ti? 0.32 4.34 2.24 32.27 

CH 6.43 9.97 9.31 26.84 

IP 35.71 23.02 34.46 9.13 

WP 38.24 23.62 20.19 2.05 

cw 9.22 11.12 10.99 6.64 

PK 4.99 12.82 13.65 8.59 

HP 2.36 9.75 3.98 2.53 

KG 0.48 2.38 3.39 3.73 

SG 0.98 2.83 0.82 4.24 

CK 1.19 0.09 0.46 1.72 

AL 0.08 0.01 0 0.21 

a

from TI Long River Survey data (Marcellus 1977)

bfrom Tf Long River and beach seine survey data (Marcellus 1977) 
111-6 

--\ 

Table 111-2. Proportlonal Distributions, Expressed as Percentages, of 

Entrainable Life Stages of White Perch as Used in the ETM Analyses 

_ _ ~~ ~ -~__ ~

Yolksac Post yolksac Entrainablc 

Year Region ma larvaa larvaa jwenlleb '­

,--, 

1974 YK 0 0.06 0.17 0 

TZ 8.07 17.56 4.38 2.76 

c�I 

IP 

47.03 

1.37 

12.36 

2.89 

3.58 

15.50 

1.18 

2.02 .,--. 

wp 5.18 6.02 12 34 1.57 

cw 4.10 4.24 10.19 3.85 

PK 3.91 6.99 19.32 11.89 

HP 0.63 13.09 13.66 ._ 8.24 

KG 1.53 13.40 15.34 15.98 

SG 7.91 14.85 3.12 42.25 

CK 4.47 6.93 2.29 6.89 

AL 15.80 1'. 61 0.11 3.37 

1975 YR 0 0.22 0.37 0.31 

TZ 21.01 25.03 6.47 3.70 

w 2.75 7.67 5.60 2.85 

IP 2.48 6.21 13.93 3.23 

WP 4.58 5.55 9.71 4.91 

cw 6.88 4.71 7.17 7.48 

PK 25.66 9.87 13.91 16.30 

HP 1.87 11.19 14.31 24.50 

KG 5.58 5.40 9.17 17.17 

SG 11.84 9.78 12.79 13.i8 

CK 16.19 10.05 6.35 5.67 

AL 1.16 4.32 0.22 0.71 

afrom TI Long River Survey data (Marcellus 1977) 

bfrom TI Long River and beach seine survey data (Marcellus 1977) 
111-7 

Table 111-3. Proportional Distributions, Expressed as Percentages, of 

Entrainable Llfe Stage of Alosa spp. (Blueback Herring and Alewife) 

as Used in the ETM Analyses 

Year Region Egga 

Yolksac 

larvaa 

Post yolksac

larvaa 

Entrainable 

j uvenileb 

1974 YK 0 0 0.02 0.15 

TZ 0 0.10 0.49 0.20 

CH 0 0.17 0.47 1.42 

IP 0.02 0.03 1.52 0.08 

UP 0.02 0.34 1.79 5.22 

cw 0.01 0.18 4.24 7.61 

PR 0.22 9.39 9.11 7.48 

HP 0.62 2.36 7.94 6.34 .KG 0.79 8.91 21.22 28.29 

SG 3.93 20.27 26.98 3.45 

CK 5.29 21.71 17.09 34.66 

AL 89.10 36.54 9.13 5.10 

1975 YK 0 0 0.02 0 

TZ 0 0.06 0.22 0 

CH 0 0.10 0.19 0 

IP 0.04 0.63 1.21 0.12 

WP 1.32 1.61 5.51 3.37 

cw 1.36 3.19 8.41 7.55 

PK 18.43 4.33 6.67 7.91 

HP 7.08 2.23 8.60 4.28 

KG 4.67 8.82 10.91 24.30 

SG 29.07 13.76 24.77 13.48 

CK 23.34 29.40 24.96 38.71 

AL 14.69 35.87 8.53 0.28 

afrom TI Long River Survey data (Marcellus 1977) 

bfrom TI Long River and beach seine survey data (Marcellus 1977, 1979) 
111-8 

-. 

Table 111-4. Proportiona Distributions, Expressed as Percentages, of 

Entrainable Life Stages of American Shad as Used in the ETM Analyses 

Yolksac Post yolksac

Year Region Egga larva” larvaa 

~ 

1974 YK 0 0 0 

TZ 0 0 0.37

ai 0 0 0.28 

IP 0.06 0 0.54 

WP 0 0.71 0.62 

cw 0 0.60 1.01 

PK 2.35 10.95 5.70 

HP 1.86 7.05 9.81 

KG 5.71 19.83 27.03 

SG 60.25 41.22 36.36 

CK 29.50 19.64 16.84 

AL4 0.27 b 1.44 

a

from TI Long River Survey data (Marcellus 1977) 

-* Entrainable 

juvenileb 

? -\ 

2.68 

11.90 + ­

9.95 

9.14 

10.71 

14.08 

19.99 

6.95 F

5.73 

3.01 

3.08 

2.78 

bfrom TI Long River and beach seine survey data (Marcellus 1977) 
111-9 

Table 111-5. Proportional Distributions, Expressed as Percentages, of 

Entrainable Life Stages of Atlantic Tomcod as Used in the ETM Analyses 

Yolksac Post yolksac Entrainable 

Year Region Egga lamab larvab juvenileb 

1975 YK 15.02 58.81 42.20 

TZ 29.74 33.87 34.18 

CH 28.64 4.89 6.68 

IP 12.22 2.03 8.78 

WP 8.36 0.27 5.16 

cw 2.83 0.09 1.56 

PK 3.19 0.04 1.20 

HP 0 0 0.13 

KG 0 0 0.02 

SG 0 0 0.01 

CK 0 0 0 

AL 0 0 0 

data available 

bfrom TI Long River Survey data (Marcellus 1977) 
r --. 

111-10 

Table 111-6. Proportional Distributions, Expressed as Percentages, of 

Entrainable Life Stages of Bay Anchovy as Used in the ETM Analyses 

-\ 

rl 

I-

_ -

Yolksac 

Year Region Egga larvaa 

1974 YK 

TZ 

CH 

IP 

WP 

cw 

PK 

HP 

KG 

SG 

CK 

AL 

1975 YK 

TZ 

CH 

IP 

WP 

cw 

PK 

HP 

KG 

SG 

CK 

AL 

70.72 23.03 

21.19 68.00 

4.62 8.97 

2.80 0 

0.67 0 

0 0 

0 0 

0 0 

0 0 

0 0 

0 0 

0 0 

49.24 0 

40.13 74.27 

3.98 25.73 

6.40 0 

0.25 0 

0 0 

0, 0 

0 0 

0 0 

0 0 

0 0 

0 0 

Post yolksac 

larvaa 

14.97 

42.49 

24-68 

15.53 

1.34 

0.58 

0.23 

0.03 

0.03 

0.01 

0.01 

0 

18.01 

37.20 

14-67 

16.93 

10.42 

2.43 

0.28 

0.03 

0.01 

0.02 . 

0 

0 

Entrainable 

juvenileb 

9.25 

35.04 

38.71 

10.60 

1^.04 

5.13 

0.10 

0.02 

0.07 

0.01 

0.03 

0 

16.84 

50.22 

12.99 

2.93 

6.38 

9.99 

0.48 

0.02 

0.09 

0.06 

0 

0 

afrom TI Long River Survey data (Marcellus 1978) 

bfrom TI Long River and beach seine survey data (Marcellus 1978) 
111-11 

Table 111-7. Dates of TI Beach Seine Collections (Day Only)a Used in 

Estimating the Proportional Distributions of the Entrainable Life 

Stages of Selected Hudson River Fish Populations 

Population 

Striped bass 

White perch 

Alosa spp. - b

American shad 

Bay anchovy 

1974 

1975 

1974 

1975 

1974 

1975 

1974 

1974 

1975 

Dates 

6/16 - 8/10

6/15 - 8/9 

6/30 - 8/10 

6/15 - 8/9 

6/1 - 819 

6/15 - 819 

612 - 8/10 

7/13 - 10/18

7/13 - 11/1 

=provided to EPA by Marcellus (1977, 1979) 

bBlueback herring and alewife combined 
CHAPTER IV 

DURATIONS OF THE ENTRAINMENT INTERVALS 

AND ENTRAI"T PERIODS FOR SIX 

HUDSON RIVER FISH POPULATIONS 

Testimony of 

John Boreman, Ph.D. 

National Power Plant Team 

U.S. Fish and Wildlife Service 

2929 Plymouth Road 

Ann Arbor, Michigan 48105 

PREPARED FOR THE 

U.S. ENVIRONMENTAL, PROTECTION AGENCY, REGION I1 

May 1979 
1. INTRODUCTION 

The total period during which entrainable organisms are present 

within the water body is termed the entrainment period. This period is 

the combination of the spawning period (period of egg deposition) and 

the average amount of time an individual takes to grow through its 

eatrainable life stages. The latter period of time is termed the entrainment 

interval. 

The vulnerability of an entrainable life stage of a given fish 

population to entrainment mortality depends, among other factors, on the 

duration of that life stage and the calendar time period during which 

members of that life stage are present in the water body. A life stage 

may be present in the water body much longer than its specified duration. 

This phenomenon occurs when spawning is not instantaneous, but is spread 

over an extended period. If physical conditions within the water body, 

such as power plant water withdrawal flow rates, vary during the entrainment 

period, then individuals of a given life stage may experience different 

entrainment mortality rates depending on when during the entrainment 

period they were spawned. 

The Empirical Transport 'Model (ETM) handles the phenomenon of 

extended spawning by tracking the entrainment mortality of each cohort 

through its entrainment interval. A cohort is defined as a group of 

individuals spawned during the same model time step (week). The total 

conditional entrainment mortality rate is the combined conditional 

entrainment mortality rates of the cohorts, weighted for the fraction 

of the .total number of individuals spawned during the entrainment period 

that each cohort represents. 

2. DERIVATION OF PARAMETER VALUES 

Average durations of entrainable life stages of selected fish 

populations inhabiting the Hudson River used in the ETM were derived 

IV-1 
IV-2 

from literature sources and field data, as presented in the testimony on 

life histories of the populations (Boreman 1979). When a range of 

average life stage durations for a population was presented in the life 

histories testimony, the minimum value was used in the ETM. If these 

minimum values are below the actual average life stage durations, then 

the ETM will underestimate the conditional entrainment mortality rate of 

that life stage (with all other input variables held constant). The 

average entrainment interval for each fish population is the sum of the 

average durations of each entrainable life stage. 

In the ETM analyses, the average entrainment interval is considered 

constant throughout the entrainment period for each population. Use of 

an average entrainment interval in the ETM underestimates the entrainment 

intervals of the early cohorts and overestimates the entrainment intervals 

of the later cohorts In each fish population modeled. If Conditions 

that affect entrainment mortality vary during the entrainment period, 

then use of an average entrainment interval may lead to an overestimate 

or Underestimate of the true conditional mortality rate, depending on 

hov the conditions vary. However, only the egg (and in the case of 

striped bass the yolksac larval) life stage durations can be directly 

related to a physical condition in the water body (water temperature); 

durations of other life stages in relation to physical and biological 

conditions within the water body cannot be quantified due to lack of 

sufffcient data. Therefore, average life stage durations are used 

for the older life stages and, as shown in the testimony on 

life histories (Boreman 1979), variation in egg and yolksac larval life 

stage durations from average values is relatively small (generally less 

than one day). 

The entrainment period for each selected fish population, as it is 

used in the ETM analyses, begins when the first entrainable life stage 

appears in field samples and ends when the last cohort has reached the 

first non-entrainable life stage. 

". 
IV-3 

The spawning period �or a fish population, as used in the ETM, is 

the number of weeks that eggs of that population were present in the 

Texas Instruments, Inc. (TI) Long River Survey samples for a given year. 

The proportion of eggs spawned during each week of the spawning period, 

and hence the proportion of the initial number of individuals represented 

by each cohort, is assumed equivalent to the estimated standing crop of 

eggs for that week divided by the estimated total standing crop of eggs 

summed over all weeks of the spawning period for that year. This method 

of calculating the temporal distribution of egg deposition does not 

work if sampling was conducted mare than once per week (which it WBS 

not) or if the egg incubation period for a given population is longer 

than one week. Of the five fish populations for which a spawning period 

is used in the ETM analyses (tomcod egg entrainment is not calculated), 

the average egg incubation periods are equal to or less than one week, 

although Alosa spp. may have an egg incubation period as long as 10 days 

very early in the season (Table 14, Boreman 1979). 

3. PARAMETER VALUES FOR SELECTED FISH POPULATIONS 

Durations of the entrainable life stages of selected fish populations 

inhabiting the Hudson River are presented in Table IV-1. The valces for 

each population were derived in the testimony on life histories (Boreman 

1979) and represent average durations for the time periods when those 

life stages were present in the Hudson River. Where possible, values 

are specified separately for 1974 and 1975. 

The 1974 and 1975 parameter values used in the ETM analyses for 

durations of the spawning periods and temporal deposition of eggs of the 

selected fish populations are listed in Tables IV-2 to IV-6 for striped 

bass, white perch, Alosa spp. (blueback herring and alewife), American 

shad, and bay anchovy. Since the 1974 Long River Survey began too late 

to sample Atlantic tomcod yolksac larvae, only 1975 data were used. The . 

ETM analyses assume all Atlantic tomcod were recruited to the yolksac 

larval life stgge before the first week of sampling in 1975. The peak 
IV-4 

5 

XO 

ms 

mu 

em 

d 

0 

=A 

U 

rd

4 

a 

a 

0

EA 

hI 

4

f 

uu 

m 

N. 

d 

In 

.N 

N 

U 

c 

d.. 

v 

urn m hb 

m m E drl GI 

c,

Q 

a

M 

Q

U 

m 

01 m yc bo

4 bo

J w 

rld 00 NN uu 

mm uu I-h 

I . 

la0 IN Id Id 

hl U N m 

UI 4 1 Wl 0 1 

N N \D 

n 

a\ 

h 

m 

d 

W 

U e 

0 

&I 

u.c 

C 

0)

& 

ad m 

0 Y 

01 (d 
IV-5 

Table IV-2. Proportion of Striped Bass Eggs Spawned Eagh Week 

During 1974 and 1975 as Used in the ETM Analyses 

Pear Week . Proportion 

1974 4/23 - 515 0.0025 

516 - 5/12 0.1226 

5/13 - 5/19 0.4189 

5/20 - 5/26 0.3875 

6/3 - 6/9 

6/10 - 6/16 

6/17 - 6/23 

6/24 - 6/30 

0.0047 

0.0046 

0.0034 

0.0017 

1975 5/11 - 5/17

- 0.0309 

5/18 5/24 

5/25 - 5/31 

611 - 617 

0.4952 

0.4115 

0.0470 

6/8 - 6/14

- 0.0008 

6/15 6/21 

6/22 - 6/28 

0.0052 

0.0094 

5/27 - 6/2 0,0541 

afrom Table .2 in Boreman (1979) 
IV- 6 

Table LV-3. Proportion of White Perch Eggs Spawned Eack Week 

During 1974 and 1975 as Used in the ETM Analyses‘ I f 

Year Week Proportion 

1974 516 - 5/12 0.0001 

5/13 - 5/19 0.0433 

5/20 - 5/26 0. I626 

5/27 - 612 0.0950 

613 - 619 0.3633 

6/10 - 6/16 0.3153 

6/17 - 6/23 0.0106 

6/24 - 6/30 0.0091 

711 - 715 0.0007 ,­

1975 5/4 - 5/10 0.0012 /“ 

5/11 - 5/17 0.0382 

5/18 - 5/24 0.1567 

5/25 - 5/31 0.6149 

611 - 617 0.0917 

6/8 - 6/14 0.0160 

6/15 - 6/21 0.0737 

6/22 - 6/28 0.0073 

6/29 - 715 0.0003 

afrom Table 9 in Boreman (1979) 
IV-7 

Table IV-4. Proportion of Alosa spp. (Blueback Herring and Alewife) Eggs 

Spawned Each Week During 1974 and 1975 as Used In the ETM Analysesa 

Year 

1974 

1975 

Week 

4/29 - 5/5 

516 - 5/12 

5/13 - 5/19

5/20 - 5/26 

5/27 - 612 

6/3 - 619 

6/10 - 6/16 

4/21 - 4/27 

4/28 - 513 

514 - 5/10

5/11 - 5/17

5/18 - 5/24 

5/25 - 5/31

6/1 - 6/7 

618 - 6/14

6/15 - 6/21 

Proportion 

0.0052 

0.0134 

0.0598 

0.8370 

0.0243 

0.0583 

0.0020 

0 0012 

0 

0.1237 

0.1631 

0.4300 

0.2779 

0.0023 

0.0001 

0 0017 

afrorn Table 14 in Boreman (1979) 
IV-8 

Table IV-5. Proportion of American Shad Eggs Spawned Each Week / / 

During 1974 a8 Used in the ETM Analysesa ,--

Year Week Proportion 

1974 4/22 - 4/28 0.0577 

4/29 - 5/5 0.1692 

5/6 - 5/12 0.0826 

5/13 - 5/19 0.0722 - 5/20 - 5/26 0.1687 

5/27 - 6/2 0.3862 

613 - 619 0.0552 

6/10 - 6/16 0.0080 ­

6/17 - 6/23 0.0002 

?' 

afrom Table 21 in Boreman (1979) 

_ -
IV-9 

Table IV-6. Proportion of Bay Anchovy Eggs Spawned Each Week 

During 1974 and 1975 as Used in the ETM Analysesa 

Year 

1974 

1975 

Week 

613 - 6/9 

6/10 - 6/16 

6/17 - 6/23 

6/24 - 6/30

711 - 717 

718 - 7/14 

7/15 - 7/21 

7/22 - 7/28 

7/29 - 814 

815 - 8/11 

8/12 - 8/18 

611 - 617 

6/8 - 6/14 

6/15 - 6/21 

6/22 - 6/28 

6/29 - 715 

716 - 7/12 

7/13 - 7/19 

7/20 - 7/26 

7/27 - 812 

Proportion 

0.0078 

0.5254 

0.1486 

0.0047 

0.0035 

0.0569 

0.1279 

0.0627 

0.0326 

0.0118 

0.0181 

0.2784 

0.0463 

0.0096 

0 

0.3012 

0.2838 

0.0689 

0.0098 

0.0020 

a

from Tables 27 and 28 In Boreman (1979) 
IV-10 

estimated weekly standing crop of Atlantic tomcod yolksac larvae occurred 

during the first week of sampling in 1975 (Table 26, Boreman 19791, 

which supports the validity of this assumption. The entrainment periods 

of the six fish populations selected for ETM analyses are listed in 

Table IV-7. 

4. REFERENCES CITED 

Boreman, J. 1979. Life histories of seven fish populations that 

inhabit the Hudson River estuary. 94 pp. Exhibit EPA-198. 
IV-11 

Table IV-7. Entrainment Period Durations for Selected Fish Populations 

Inhabiting the Hudson River During 1974 and 1975 as Used in the ETM Analyses 

Entrainment Spawning Entrainment 

Population Year interval (days) period (days) period (dates)a 

Striped bass 1974 

1975 

65.5 

63.5 

63 

49 

4129 - 8/29 

5/11 - 8/25 

White perch 1974 

1975 

63 

69.5 

63 

63 

516 - 9/2 

5/4 - 917 

1974 

1975 

b Alosa spp. 63 

63 

49 

63 

4/29 - 8/12 

4/21 - 8/18 

American shad 1974 60 63 4/22 - 8/16 

Atlantic tomcod 1975 91 -C 319 - 6/8 

Bay anchovy 1974 

1975 

74 

74 

37 

63 

6/3 - 10125 

6/1 - 1019 

abeginning of entrainment periods are based on dates when eggs (or 

tomcod yolksac larvae) were first sampled in the TI Long River surveys, 

from Boreman (1979) 

bblueback herring and alewife 

Csingle cohort used due to lack of data on the temporal distribution 

of Atlantic tomcod egg deposition 
CHAPTER V 

W-FACTORS FOR HUDSON RIVER ICHTHYOPLANKTON ENTRAINED AT 

BOWLINE, LOVETT, INDIAN POINT, ROSETON, AND DANSKAMMER 

TESTIMONY OF 

LAWRENCE W. BARNTHOUSE, PheD. 

K. DEVA KUMAB, Ph.D. 

AND 

BERNADETTE La KIRK 

ENVIRONMENTAL SCIENCES DIVISION 

OAK RIDCE NATIONAL LABORATORY 

OAK RIDGE, TENNESSEE 

PREPARED FOR THE UNITED STATES ENVIRONMENTAL PROTECTION AGENCY 

REGION I1 

APRIL, 1979 
SUMMARY 

This chapter contains estimates of W-factors developed from 

ichthyoplankton data collected at the Bowline, Lovett, Indian Point, 

Roseton, and Danskamner generating stations. Two sets of W-factors 

were developed for each of five populations: striped bass, white perch, 

Atlantic tomcod, bay anchovy, and -Alosa (alewife and blueback herring).

The chapter consists of five parts: 

1. 

2. 

3. 

4. 

5. 

An introductory discussion of several methodological problems 

associated with all estimates of W-factors and of our approach 

to dealing with these problems: the calculation of two sets 

of parameters, using two independent methods of computation. 

A description of the two methods, the Modified Utility (MU) 

and Gear Bias Cancelling (GBC) method, used to compute 

W-factors. 

Separate sets of W-factors for striped bass, white perch, 

Atlantic tomcod, bay anchovy, and Alosa computed using the MU 

and GBC methods. 

A point by point discussion of our disagreements .with the 

data and methods used by LMS in Exhibit UT-3 to compute 

w-ratios. 

A comparison between W-factors for each population calculated 

using the MU and GBC methods. 
1 INTRODUCTION 

Entrainment models employed by consultants for both the utilities 

and EPA to estimate conditional entrainment mortality rates include a 

parameter, known as the w-ratio (utilities) or W-factor (EPA). This 

parameter is intended as a measure of the abundance of organisms in 

power plant intake water relative to their average abundance in an 

idealized cross-section of the river in front of a paver plant. The 

W-factor is included in entrainment models primarily to account for the 

effects of non-uniform distribution of organisms in the river on the 

number entrained. 

If, for example, larvae are concentrated in the center of the river 

while power plants draw cooling water from near shore, then the density 

of larvae in the intake water is likely to be less than the average 

density of larvae in the cross-section of the river in front of the 

intake. The same result can occur if organisms are concentrated near 

the bottom of the river while cooling water is withdrawn primarily from 

the surface. In either case, the number entrained would be less than 

would be predicted by a model that assumes that the density of organisms 

in pawer plant cooling water is equal to the cross-sectional average 

density of organism in the river. 

There are also distributions that can result in the abundance of 

organisms in the intake water being greater than their average 

cross-sectional abundance in the river: if, for example, they 

congregate in shallow water near the shore (all Hudson River power 

plants have shoreline intakes). 

In the past it has been argued that, for entrainable life-stages 

of striped bass, the distribution of organisms in the river is such 

. that in general they are less abundant in the river strata from which 

cooling water is drawn than in the entire river cross-section, and 

therefore W-factoss should on the average be less than 1.0. Barnthouse 

et al. (1977) have sumnarized the positions of both the NRC staff and 

utility consultants at the time the Final Environmental Statement for 

Indian Point Unit 3 was prepared. Results derived from ORNL's striped 

v- 1 
-- 

v-2 

bass young-of-the-year model (developed for NRC) indicated that striped 

bass ichthyoplankton are concentrated near the river bot tom or the 

shores, where they are less susceptible to downstream transport 

(Barnthouse et al. 1977, p. 5-58). Moreover, a comparison of plant 

intake and river transect data collected at Bowline, Lovett, Roseton, 

and Danskanmer in 1973 showed that the aeasured densities of striped -

bass life-stages were generally lower in samples collected at power 

plant intakes than in samples collected at the river transect stations 

(Barnthouse et al. 1977, p. 5-53). The NRC staff concluded that 

W-factors (then called fI) for striped bass life stages were generally 

less than 1.0 but greater than 0.5 (Barnthouse et al. 1977, p. 5-60). 

Similarly, in Exhibit UT-3 both TI and LMS employ W-factors that are 

generally less than 1.0. TI assumed (Exhibit UT-3, Sections 2-VI 

and 2-VII) that W-factors for all life-stages of all species for which 

- entrainment impacts were estimated (striped bass, white perch, Atlantic

tomcod, and American shad) are equal to 0.5. LMS concluded (Exhibit 

UT-3, p. 3-LV-541, based on plant and year specific intake and river 

transect data, that both upper layer and lower layer w-ratios are 

generally less than 1.0. Most of the W-factors developed in this 

testimony are smaller than 1.0. However, a substantial percentage ate 

larger than 1.0, and the majority are larger than the value of 0.5 

assumed by TI. 

Many of the w-ratios calculated by LMS (Exhibit UT-3, 

Table 3-IV-27) are based on data that, in our opinion, should not be 

used for that purpose. More important, we believe that, because of 

biases inherent in the methods used to calculate w-ratios, and because 

of the extraordinarily low precision of all estimates of ichthyoplankton 

abundance, neither LMS' estimates nor any other single set of W-factors 

can by itself be assumed to be accurate. 

In Section 1.1 we discuss two seemingly insurmountable 

methodological problems associated with the estimation of W-factors. 

In Section 1.2 we propose our solution to these problems: the use of 

two, rather than one, independently derived methods of estimating 

W-factors. The first, referred to as the Modified Utility (MU) method, 

~ 

-

-
v-3 

is only slightly different from the method used by LMS in Section 3-IV 

of Exhibit UT-3. The second, referred to as the Gear Bias Cancelling 

(GBC) method, was developed in an attempt to eliminate biases 

introduced into estimates of W-factors by differences in the types and 

methods of deployment of the gear used in collecting plant and river 

ichthyoplankton samples. Sections 2 and 3 contain, respectively, 

descriptions of the Ku and GBC methods and the results obtained when 

the two methods are applied to data collected at Bowline, Lovett, Indian 

Point, Roseton, and Danskamner (the specific data sets used are 

described in Appendix B of this exhibit). In Section 4 we discuss our 

objections to the LMS w-ratios. 

to the data used by INS rather than to the analytical treatment of the 

For the most part these are objections 

data. A general discussion of the W-factors is presented in Section 5. 

1.1 METHODOLOGICAL PROBLEMS ENCOUNTERED IN 

ICHTHYOPLANKTON SAMPLING AND THEIR CONSEQUENCES 

WSTH RESPECT TO THE ESTIMATION OF W-FACTORS 

In sunnnarizing and criticizing the data and methods used by both 

the utilities and the NRC staff to estimate f (now called the I 

W-factor) for striped bass, Barnthouse et al. (1977; Section 5.4.3) 

pointed oct two basic problems that severely limit the accuracy and 

precision of those estimates.> The first of these is insufficient 

quantity of data, caused by insufficient plant and river sampling effort 

during periods of high abundance of striped bass ichthyoplankton, and 

by the inability of the gear to effectively sample older larvae and 

juveniles. The second is that the methods used to collect 

ichthyoplankton from the river invariably have been substantially 

different from those used to collect ichthyoplankton at power plant 

intakes. 

Although two additional years of data are now available for most 

plants, these two sources of error still plague all attempts to obtain 

accurate empirical estimates of W-factors for striped bass and other 

Hudson River fish populations. In this section we briefly discuss the 

problems caused by small sample size and lack of gear comparability and 

argue that no sampling program can completely eliminate them. 
v-4 

The collection efficiency of ichthyoplankton sampling gear , defined 

as the fraction of organisms in the path of the gear that are captured 

and counted, is strongly influenced by the design of the gear and the ~ 

manner in which it is deployed. Therefore, ichthyoplankton densities 

computed from data collected with different gears can differ 

substantially, even though the actual densities of organisms in the -

paths of the gears are identical. Since the methods used to collect 

ichthyoplankton at power plant intakes on the Hudson River are 

invariably different from those used at the river transect stations, 

W-factors computed as simple ratios of plant and river concentrations 

are subject to potentially severe biases. 

Carpenter (1979) has identified three major sources of such bias: 

gear avoidance, clogging of nets with detritus, and extrusion. 

According to Carpenter, the ability of larval and juvenile fish to 

detect the presence of towed plankton nets (the gear used to collect 

the data used to compute W-factors) and to subsequently evade capture 

is a function of the size of the net, the speed at which it is towed, ~ 

and the presence or absence of turbulence-creating structures (such as 

bridles) in front of the net. All other factors being equal, the 

probability that an organism in the path of a net will be able to swim 

out of that path before it is capture.d decreases as the diameter of the 

net opening increases. Similarly, the probability of escape decreases 

with increasing net speed because, once the net has been detected, less 

time is available for the organism to react and swim out of the path of 

the net. The presence of bridles, towlines, or other structures in 

front of the net mouth is thought to increase the probability of escape 

by enabling organisms to detect the gear at a greater distance. 

Clogging of a net with plankton or detritus reduces its filtration 

efficiency, i.e., the fraction of the water (as distinct from the 

fraction of organisms) in the path of the net that enters the net mouth 

and is filtered. According to Smith et al. (19681, clogging increases 

the incidence of gear avoidance by creating turbulence in front of the 

net. In addition to increased avoidance, clogging can lead to 

underestimates of the true density of organisms in the water sampled by 

,­

~ 

I 
v- 5 

causing overestimation of the volume of water filtered. At Bowline, 

Lovett, and Indian Point, intake sample volumes have been estimated 

indirectly from measurements of intake velocity rather than directly 

measured by placing flawmeters in the nets. 

It is possible for eggs and small larvae to burst in a net or to 

be pushed through the mesh pores and lost. 

referred to by Carpenter (1979) as extrusion, increases as the tow speed 

The magnitude of this bias, 

of the net increases because the rate of filtration per unit net area 

(and therefore the pressure with which organisms are pushed against the 

net 1 increases. 

In geceral, the nets used at Hudson River power plant intakes have 

smaller mouth openings than do the nets used to collect river transect 

samples (Carpenter 1979). Moreover, the velocity of sampling is 

invariably much higher in the river than at power plant intakes. At 

the plants, nets are simply lowered into the intake flow. Intake 

velocities at Bowline, Indian Point, and Roseton are on the average. 

less than 30 cm/sec (Carpenter 1979). In contrast, the river transect 

samples are collected by towing nets at a velocity of 60-150 cm/sec 

(Carpenter 1979, Table 3). If the effects of net size and sampling 

velocity were the only biases present, then ichthyoplankton densities 

measured at power plant intakes would be less than those measured at 

the river transect stations, even in the absence of real differences in 

ichthyoplankton abundance. However, because of the higher sampling 

velocity in the river, it is possible for life stages susceptible to 

extrusion to be sampled less efficiently in the river than at power 

plant intake stations. In addition, the nets used to collect river 

samples have bridles and towlines while the nets used to collect plant 

samples do not (Carpenter 1979). Increased gear avoidance due to the 

presence of bridles probably offsets to some degree the biases caused 

by the greater size and sampling velocity of the gear used to collect 

the river samples. 

Carpenter (1979) concluded that the overall effect of the various 

biases is an underestimation of ichthyoplankton densities measured at 

the Roseton, Bowline, and Indian Point intakes relative to densities 
V-6 

measured at the corresponding river transect stations. However, the 

possibility exists that for some life-stages of some species at some 

plants it may be the river concentrations that are underestimated. At 

present it is not possible to quantify the relative biases, and 

therefore it is not possible to compute unbiased estimates of W-factors 

using simple ratios of plant and river concentrations. 

Even if there were no gear biases, precise estimates of W-factors 

based on plant and river ichthyoplankton data would still be impossible 

to obtain. The reason for this unfortunate fact is the extremely 

"patchy" nature of ichthyoplankton distributions. By patchy we mean 

that the variation among the numbers of organisms collected in 

different samples is greater than would be expected if organisms were 

randomly distributed throughout the river. 

According to Cassie (19631, the proper technical term for this 

type of distribution is "overdispersion." Based on his review of the 

literature, Cassie concluded that planktonic organisms are nearly 

always overdispersed. In the Final Environmental Statement for Indian 

Point Unit 3 (U. S. Nuclear Regulatory Commission 19751, the NRC staff 

examined ichthyoplankton densities observed in individual samples 

collected at Bowline, LOvett, Roseton, and Danskammer in 1973. The 

staff concluded that the distribution of these densities was "strongly 

skewed toward zero." In other words, many samples contained no 

organisms while a few contained many organisms. This is a 

characteristic of overdispersed distributions 

The major consequence of overdispersion, as far as the computation 

of W-factors is concerned, is that estimates of the mean concentration 

of ichthyoplankton present at a power plant intake or in the river will 

have high standard errors, even if large numbers of samples are 

collected. We will illustrate this problem by examining several sets 

of abundance data for striped bass post yolk-sac larvae. These data 

were collected in 1975 at the Roseton intake, the Roseton/Danskaarmer 

river transect stations, and the Indian Point intake and discharge. 

r -­

., 

,-
v- 7 

The sampling program at Roseton is typical of those conducted at 

most Hudson River power plants. In Table V-1 we have tabulated mean 

densities, numbers of samples, standard errors, and 95% confidence 

intervals for striped bass post yolk-sac larvae collected at the Roseton 

intake and at the Roseton/Danskamer river stations on sampling dates 

between May 29 and June 19, 1975- Results are presented separately for 

day and night. The most precise results were obtained at night at the 

Roseton intake on June 2. As measured by the ratio of the upper and 

lower 95% confidence bounds, the actual density of striped bass post 

yolk-sac larvae (neglecting gear bias) is known mly to within a factor 

of two. On all other nights the precision of estimates of the mean 

density of post yolk-sac larvae at the Roseton intake is much lower. 

Even though a mean density of 100 larvaell000 m was observed on the 3 

night of June 5, the precision of this estimate is so low that it is 

not significantly different from zero. The daytime estimates at the 

Roseton intake are even less precise than the nighttime estimates. 

Only two of the five means are significantly different from zero, and 

the most precise (June 2) is known only to within a factor of 4.6. The 

precision of the estimates of the abundance of post yolk-sac larvae at 

the Roseton/Danskamaner river stations is lower still. None of the 

daytime means is significantly different from zero. The upper and lower 

95% confidence bounds around the two nighttime means that are 

significantly different from zero differ by greater than a factor of 6. 

The sampling program conducted at the Indian Point intakes and 

discharge canal is the most intensive conducted at any Hudson River 

power plant. Therefore, the precision of the abundance estimates 

obtained at Indian Point should be higher than that obtainable at any 

other plant. However, the intensive sampling effort at Indian Point 

appears to have prodwed estimates of the abundance of striped bass 

post yolk-sac larvae that are only marginally more precise than those 

obtained at Rosecon. During the period from May 27-June 24, 1975, 

which spans the period of peak abundance of striped bass post yolk-sac 

larvae in the Indian Point vicinity, between 36 and 69 samples were 
-- 

-- 

v-a 

Table V-1. The precision of estimates of the density of striped bass post.yolk-sac larvae at the Roseton

intake and river transect stations, 1975a 

Densi tyc 

Mw im 

ROSETON INTAKE (DAY) 

12.8 

622.0 

1762.3 

582.5 

16.6 

14.8 

ROSETON INTAKE (NIGHT) 

7.7 

612.2 

157.1 

467.7 

30.3 

ROSETON INTAKE R DAY)^ 

66.9 

187.2 

565.9 

364.2 

12.7 

Ratio of 

95% confidence upper and 

bounds lower bounds 

0-5.4 

53.3-246.1 

138.4-867.0 

0-489.7 

0-12.5 

0-9.0 

0-10.4 

320.0-626 -8 2.0 

0-316.5 

201-3-610.7 3.0 

13.9-44.3 3.2 

0-43 -5 

0-126.1 

0-340.5 

0-262.5 

0-7.4 

0-340.2 

143.9-959.9 

123 -6-808.1 

0-1088.2 

0-8.4 

rStandard error 

of mean 

1.6 

45.9 

165.6 

99.2 

3.3 

2.5 

1.7 

63.9 

50.3 

47.6 

1.2 

9.4 

27.4 

77.2 

57.8 

1.a 

Date Nb Mean Minimum 

May 29 8 1.6 0 

June 2 18 149.7 0 

June 5 12 502.7 0 

June 9 6 231.8 0 

June 12 5 3.3 0 

June 19 6 2.5 0 

May 29 5 2.8 0 

June 2 8 473.4 66.7 

June 5 3 100.2 0 

June 9 3 406.0 312.5 

June 19 2 29.1 27.9 

May 29 7 20.0 3.5 

June 2 7 57.6 0 

June 5 7 147.5 0 

June 9 7 118.0 0 

June 19 7 2.9 0 

May 29 7 113.2 0 

June 2 7 551.9 100.6 

June 5 7 465.8 153.7 

June 9 7 466.4 49.0 

June 19 7 3.6 0 

ROSETON INTAKE (N1GHT)d 

652.7 90.8 

1340.5 163.2 

1207.5 136.9 

1907.2 248.7 

12.6 1.9 

'Source of data is described In Appendix B. 

bTotal number of samples collected (depths and stations combined). 

'Number of organisms per 100 m3. dAll transect stations cmbined. 

_-
v-9 

collected every night from the Indian Point Unit 2 intake and between 

18 and 71 were collected from the discharge canal (Table V-2). Even 

with this level of effort, the standard errors associated with estimates 

of mean abundance on each date are large. On the dates on which the 

most precise estimates were obtaiced (June 17 at the intake and June 3 

at the discharge canal), the mean abundance of striped bass post 

yolk-sac larvae is known only to within a factor of 2. On the date on 

which the highest abundance was observed (June 101, the mean densities 

(neglecting gear bias) are known only to within a factor of 3.5 at the 

intake and 4.5 at the discharge. Although we have not performed the 

above analysis for all life-stages of all species collected at all 

plants during all years for which data are available, there is no 5 

priori reason to expect other ichthyoplankton abundance estimates to be 

more precise than are those that we have examined. 

All estimates of W-factors obtained from this data, both ours and 

those of the utilities, are low in precision. Even large numerical 

deviations from 1.0 may be attributable to sampling error, as can 

between-year differences in the calculated W-factors for the same 

population and life stage. No single analytical method can completely 

overcome the inherent limitations on accuracy and precision caused by 

non-comparable sampling methods and the patchy nature of ichthyoplankton 

distributions. 

1.2 WKY TWO ESTIMATORS ARE BETTER THAN ONE 

In a well-known and frequently cited paper, Levins (1966) discussed 

the dileuma faced by a biologist attempting to model the evolution of a 

hypothetical plant or animal population (e.g., the Hudson River striped 

bass stock). Such populations, composed of individual organisms of 

different ages and genetic constitutions, interact with a heterogeneous, 

fluctuating environment and with other populations (e.g., their 

competitors, predators, and prey) that are themselves evolving. Levins 

concluded that biological systems are so complex and poorly understood 

that it is not practical to attempt to build a single model that 

faithfully reflects all of the bewildering complexity that characterizes 
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v-11 

the interaction of an evolving population with its environment. As a 

solution to the dilemma, he proposed the use of clusters of models: 

alternative models of the same phenomenon, each constructed using a 

different set of simplifying assumptions. If only one such model were 

used, it would not be possible to determine whether results derived 

from the model were realistic or whether they were artifacts introduced 

by the simplifying assumptions. Suppose, however, that two or more 

independent models, each simplified in a different way, lead to similar 

results. In this case it is possible to have some confidence that the 

realistic aspects of the models, rather than the simplifications, are 

responsible for those results. Truth, Levina suggested, can be found 

at the "intersection of independent lies ." 

Although proposed for a different problem than the one considered 

here, Levins' solution is as valid an approach for estimating W-factors 

ae it is for studying the evolution of populations. The method used by 

US to compute w-ratio8 is simplified, in that the sampling gear used 

to collect ichthyoplankton from the river and from power plant intakes 

and discharges are assumed to be equally efficient at collecting 

organisms, regardless of differences in size, structure, and means of 

deployment. Since the gears undoubtedly differ in efficiency in ways 

that have not been (and perhaps cannot be) accurately measured, the 

w-ratios derived using US' method are influenced by biases that cannot 

be quantified. Such biases could cause results obtained from the Beal 

The Life Cycle model to be seriously in error. 

In order to ensure that results obtained from the Empirical 

Transport Model (and especially resource management decisions based on 

thoee results) are not overly influenced by errors in the estimation of 

W-factors, we developed not one, but two sets of W-factors. The two 

sets were obtained using independent methods, each of which employs 

different simplifying assumptions. Our first method, the Modified 

Utility o!lU) method, is conceptually identical to the approach taken by 

LMS in that the collection efficiencies of all sampling gears are 

assumed to be equal. There are no major differences between the tvo 

. .. 
v- 12 

other than the fact that one is used to compute w-ratios for a 

2-layered model of the river and the other to compute depth-averaged 

W-factors for a single-layered model. 

Our second method, the Gear Bias Cancelling (GBC) method, is 

entirely different in concept. The efficiencies of the gears used to 

collect ichthyoplankton from the river and from power plants are 

assumed to be different, and ‘the computational procedure is designed 

specifically to cancel out biases caused by these differences. 

Simplifying assumptions about the distribution of organisms in the . 

river and about the flow of water into a power plant intake are 

employed as part of the GBC method, and these assumptions may introduce 

biases into the results. However, such biases are likely to differ in 

magnitude and/or direction from the biases associated with W-factors 

calculated using the MU method. Similarly, W-factors computed using 

these two methods are influenced in different ways by sampling errors 

(the sensitivities of the MU and GBC W-factors to biases and sampling 

errors are discussed in Section 5). If conditional entrainment 

mortality rates obtained from the Empirical Transport Model are 

similar, regardless of which set of W-factors is used, then we have 

some confidence that the ETM results are not seriously compromised by 

errors in our estimates of this parameter. 
V-13 

2. METHODS USED TO COMPUTE W-FACTORS FOR HUDSON RIVER POWER FLANTS 

In this Section we describe the two methods used to compute 

W-factors for Hudson River ichthyoplankton entrained at the Bowline, 

Lovett, Indian Point, Roseton, and Danskammer power plants. Our two 

methods are similar to the utilities' method in several respects. 

First, separate W-factors are computed for each life-stage (eggs, 

yolk-sac larvae, post yolk-sac larvae, and, for some species, 

juveniles) of each population. Second, U-factors are computed 

separately for day and night, with day and night having the definitions 

set forth by the utilities on p. 3-IV-32 of Exhibit UT-3. Third, in 

general only paired sets of plant and river data collected on the same 

dates were used. Unlike the utilities' w-ratios, which are computed 

separately for upper and lower layers of the river and the power plant 

intake bays, our W-factors are averaged over all depths. Moreover, 

unlike the utilities, we included data collected on all dates on which 

organisms were caught in either the plant or the river, regardless of 

how law the densities might have been (this procedure expands the data 

base slightly and should, in theory, slightly increase the precision of 

the resulting W-factor estimates). 

2.1 MODIFIED UTILITY (MU) METHOD 

Three steps are involved in the computation of a W-factor for a 

given species and life-stage using the MU method: 

(1) calculation of average plant and river densities for each 

sampling date on which the life-stage was collected, 

(2) calculation of mean seasonal plant and river densities, i.e., 

the average densities over all the sampling dates on which 

the life-stage was collected, and 

(3) calculation of the W-factor as a ratio of the seasonal plant 

and river densities. 
V- 14 

A number of samples (sometimes a large number) are collected at 

each location (plant or river) during each time period (day or night). .- River samples are collected at several stations; samples are collected .I 

at three depths both in the river and at the parer plants. Several 

sets of plant samples may be collected during a given day or night. We 

treat all plant or river samples collected on the same date during the 

same time period as if they are replicate samples drawn from a common 

sample poplation. All of these samples are pooled in order to 

calculate the average day or night plant or river density of a 

particular fish population and life-stage. That is, the average 

density is equal to the sum of the numbers of organism collected in 

all of the samples divided by the sum of the sample volumes: 

m 

r "., 

where 

"ij k number of organisms caught in the kth sample collected 

at location i (plant or river) on date j, 

Vijk = volume of kth sample collected at location i on date j, 

m = number of samples collected at location i on date j during r_ 

either daytime or nighttime, 

n.. sum of the n..

13 1Jk 

'8 over all values of k, 

V = sum of the v.. 's over all values of k. ij. lJk 

The above computational procedure is equivalent to weighting each 

sample according to its volume, on the assumption that the greater the 

volume of a sample, the more reliable the resulting density estimate. -

In order to demonstrate this, we define a weighting factor, 

w.. t vijk I ­

ijk v.. 

1J .. 
V-15 

If d.. ljk/v..

Ilk (= n.. ijk is the density of organisms in a particular 

sample, then we can define a eighted average density as 

m 

D!. = C wijkdijk . (v-2)

'Jk k5;1 

From the definitions of Vijk and dijk, 

m 

m 

C nijk/v;. k=l 13. 

a jij. /V..ij. 

= 0..

13 

Given the plant and river densities for each date, the seaaonal plant 

and river densities ar computed as unueighted averages of the 

respective D..'s. Finally, the W-factor is computed as the ratio of

U 

the seasonal plant and river densities. Since only paired sets of . 

plant and river data are used to calculate W-factors, the number of 

sampling dates used to compute the eeasonal plant density is always 

equal to the number used to compute the seaso'nal river density. 

Theref ores 

07-31 

where 

D1, sum of plant densities over all dates', 

D2. = sum of river densities over all dates. 

The seasonal W-factors are, equivalent to weighted averages of 

W-factors computed separately for each date, with the weighting factor 
V- 16 

assigned to each date being a function of the density of organisms in 

the river on that date. In order to demonstrate this we can rewrite 

Eq. (V-3) as: 

T 

W = f C D2j(Dlj/D2j)l/D2.

j=1 

T 

(v-4) 

T 

= c w~(D~~/D~~) ,

j=1 

where 

T = number of sampling dates on which organisms were collected, 

w 

j = D2j/D2. = weighting factor for date j. 

The quotient (D 1 J./D2J.), the ratio of plant density to river density 

on date j, is simply an estimate of the W-factor OG date j. The 

weighting factor, wj, is the ratio of the river density on date j to 

the sum of river densities over all dates. If the life-stage in 

question were very abundant on date j relative to its abundance on 

other dates, the weighting factor would be large. If, on the other 

hand, the density in the river were relatively law on date j, the 

weighting factor would be small. Thus, our W-factor estimator gives 

the greatest weight to dates on which organisms were most abundant in . 

the river. This is a desirable property because these are the dates on 

which it is most important to know the fraction of organisms in the 

vicinity of a power plant that are entrained and killed. 

The only deviations from the computational procedure described 

above occurred in the treatment of the intake and discharge data at 

Indian Point. The sampling conditions at the intake and discharge 

stations are quite different. Different gears are used, and sampling . . 

velocities are several times higher at all of the discharge canal 

stations than at the Unit 1, 2, and 3 intake stations. Therefore, the 
V-17 

intake and discharge data were not treated as if drawn from a common 

sample population. Instead, intake and discharge densities on each 

date were computed separately using Eq. (V-1). Like the utilities 

(Exhibit UT-3, p. 3-IV-371, we then computed plant densities for each 

date as unweighted averages of the intake and discharge densities. The 

W-factors were then computed from fEq. (V-3)1. 

2.2 GEAR BIAS CANCELLING (GBC) METHOD 

Because of differences in the types and methods of deployment of 

sampling gear between river and plant, direct comparisons between the 

calculated plant and river abundances probably do not yield unbiased 

estimates of W-factors. Although the differences in sampling techniques 

should generally result in relative underestimates of plant abundance 

(Carpenter 19791, it is conceivable that in some cases (e.g., because 

of greater extrusion in river samples) it is the river abundance that 

is underestimated. Because the magnitude and direction of biases due 

to differences in sampling technique cannot be quantified, we have 

attempted to eliminate the problem of gear bias by developing a new 

method of estimating W-factors. This method, called the Gear Bias 

Cancelling (GBC) method, is based on a two-layered model of the river 

cross-section that is essentially identical to that used by LMS as the 

basis for the w-ratios presented in Section 3-IV of Exhibit UT-3. The 

upper and lower layers of the river are defined, respectively, as the 

water masses lying above and below an imaginary boundary exactly midway 

between the surface and bottom, from shore to shore. The upper and 

lower layers of an intake bay are defined similarly. 

Like the calculation of LMS w-ratios, the'calculation of W-factors 

using the GBC method involves comparing densities of organisms observed 

in the upper (or lower) layer of an intake bay to corresponding 

densities observed in the upper (or lower) layer of the river. Thus, 

as an intermediate step in our procedure, we calculate w-ratios that 

are identical in concept to those of the utilities. However, rather 

than applying OUT w-ratios directly to a two-layered version of the 

Empirical Transport Model, we use them to compute depth-averaged 
V-18 

W-factors in which the biases caused byunequal plant and river gear ._ 

efficiencies are cancelled out. In order to demonstrate the bias 

cancelling principle that is the basis for. the GBC method, we define: 

Dw,Dm,DpL,DEU. = actual densities of organisms in the upper 

and lower layers of plant and river, 

e = efficiency of plant sampling gear, 

f = efficiency of river sampling gear, 

eDmrfDIUl,eDpL,fDm= observed densities of organiems in the upper. 

and lower layers of plant and river. 

From the definition of a w-ratio (Exhibit UT-3, p.' 3-IV-30), 

wU = eDPu/fDw and wL = e DPL/fDm 

The w-ratios, as calculated directly from the observed plant and river 

densities, are biased estimators of the ratios of actual plant and 

river upper and lower layer densities. However, if the ratio of 

v-ratios is calculated, the gear bias. terms e and f cancel out: 

(v-5 1 

In the GBC method, ratios of upper and lover layer w-ratios are used to 

compute W-factors that are independent of biases caused by unequal 

plant and river sampling efficiencies. 

The GBC method employs simplifying assumptions about the 

distribution of organisms in the upper layer of the river and about the 

flow of water into a power plant intake. For this reason we use it as 

a complement to, rather than a replacement for, the Modified Utility 

(MU) method. Moreover, since the Bowline plant draws water from 

Bowline Pond rather than directly from the river, it is unlikely that 
v-19 

the upper and lower layers of the Bowline intake bay are in any way 

related to the upper and lower layers of the river. For this reason 

the GBC method is not used to estimate W-factors for Bowline. For this 

plant, the Modified Utility method can be used to compute estimates of 

W-factors that are free from gear bias, using the Bowline Pond 

ichthyoplankton data instead of intake data. 

The assumptions required by the GBC method are discussed in detail 

in Section 2.2.1; the computational procedure is explained in 

Section 2.2.2. 

2.2.1 Discussion of Assumptions 

The GBC method is based on four assumptions: 

(1) There are no lateral trends in the abundance of organisms in 

the upper layer of the river. Such trends may, however, 

exist in the lower layer. 

(2) There is no active avoidance of the intake structure by

(3) 

(4) 

entrainable ichthyoplankton. 

Power plants draw 57% of their cooling water from the upper 

layer of the river and 43% from the lower layer. 

All water in the upper layer of an intake bay is drawn from 

the upper layer of the river. Water in the lower layer of an 

intake bay cmes from both layers of the river. 

The first assumption, that of absence of lateral trends in 

abundance in the upper layer of the river, is consistent with data on 

the lateral distribution of ichthyoplankton reported in Exhibits UT-4, 

UT-6, and UT-?. In Sections 7.4.1.5.1 and 7.4.2.6.1 of Exhibit UT-4, 

TI reported results obtained from studies of the lateral distribution 

of striped bass yolk-sac and post yolk-sac larvae in the Cornwall 

vicinity. The lateral distribution of yolk-sac larvae at the surface 

and at the bottom was studied on May 22-23 and May 28-29, 1975. On 

May 22-23, no statistically significant differences in density betveen 

east, channel, or west zones or between surface and bottom strata were 

found (Exhibit UT-4, Fig. 7.4-8). On May 28-29, the surface density of 

yolk-sac larvae was significantly lower in the channel zone than in the 
v-20 

west zone. The lateral distribution of post yolk-sac larvae was 

studied on May 28-29 and on June 13-14, 1975. Although statistically 

significant trends in lateral abundance were noted in the bottom layer 

on June 13-14, no such trends were found at the surface (Exhibit UT-4, 

Fig. 7 .4-18). 

Exhibit UT-6 contains information on the lateral distribution of 

white perch, striped bass, Atlantic tomcod, Alosa, and bay anchovy eggs 

and larvae in the Roseton/Danskamer vicinity from 1974 through 1976. 

No consistent lateral trends in abundance were observed at any depth 

for white perch or striped bass eggs or larvae (Exhibit UT-6, 

Tables 9.1-2, 9.1-4, 9.1-8, 9.1-10). There is some evidence for a 

trend in lateral distribution of Atlantic tomcod larvae in the 

Roseton/Danskamner vicinity (eggs were collected on too few sampling . 

dates for trends to be observed). Data obtained on five sampling dates 

are listed in Table 9.1-15 of Exhibit UT-6. Both at the surface and at 

the bottom, Atlantic tomcod larvae were most abundant at the west 

station and least abundant at the east station on three of the five 

dates. Stronger evidence for a trend in lateral abundance exists for 

Alosa eggs and larvae. Eggs have been consistently most abundant at 

the west station at all depths (Exhibit UT-6, Table 9.1-16). Larvae 

have been consistently most abundant at the east station at all depths 

(Exhibit UT-6, Table 9.1-18). A plausible hypothesis explaining the 

observed trend in larval distribution is presented on p. 9.1-55 of 

Exhibit UT-6: the higher abundance of larvae at the east station may 

be related to its proximity to the mouth of Wappinger's Creek, a 

possible spawning site for large numbers of alewife and blueback 

herring. Although the egg distribution data indicate greater Alosa 

spawning activity on the west side of the river than on the east side, 

no explanations are proposed in Exhibit UT-6. Data presented in 

Table 9.1-22 of Exhibit UT-6 indicate no lateral trends in larval bay 

anchovy abundance at any depth. 

Data presented in Tables 9.1-4 and 9.1-8 of Exhibit UT-7 do not 

' 7 ' reveal any consistent lateral distribution patterns for larval white 

perch or striped bass in the Bowline vicinity. White perch larvae 
v-21 

were, as noted on p. 9.1-12 of Exhibit UT-?, more abundant at the 

channel station than at the east or west stations on the dates of peak 

abundance in 1975 and 1976, but no such pattern was observed in 1973 or 

1974. The highest densities of eggs of both species appear to be found 

at the east and west stations (Tables 9.1-2 and 9.1-71, with lowest 

densities at the channel stations. Unlike the pattern observed at 

Roseton/Danskamner, there appears to be no east-west trend in abundance 

of Atlantic tomcod (Table 9.1-11) or -Alosa (Table 9.1-14) larvae in the 

Bowline vicinity. Few Atlantic tomcod or Alosa eggs have been 

collected at Bowline, and thus there is no lateral distribution data 

for this life-stage of either species. Although no lateral trends in 

abundance of bay anchovy eggs have been observed in the Bowline 

vicinity (Exhibit UT-7, Table 9.1-171, such a trend does exist for 

larval bay anchovy (Exhibit UT-7, Table 9.1-181. Densities of larvae 

are usually lower at the channel station than at the shallower east and 

west stations. 

In summary, our first assumption is generally supported by results 

presented in Exhibits UT-4, UT-6, and UT-7. These results indicate 

that consiszent lateral trends in abundance of eggs and larvae of the 

Hudson Riyer fish species considered in this testimony are the 

exception rather than the rule. 

There is no direct evidence with which to test our second 

assumption, that of the absence of active intake avoidance by 

ichthyoplankton. Active intake avoidance is, or course, impossible for 

eggs and probably impossible for yolk-sac larvae. It is at least 

theoretically possible for post yolk-sac larvae and early juveniles to 

detect alterations in current patterns caused by the intake flow and to 

react to these alterations. Whether detection actualiy does occur, and 

whether any of these organisms escape the intake flow once it is 

detected, are unknown. 

Our third assumption, that power plants draw 57% of their cooling 

water from the upper layer of the river and 43% from the lower layer, 

is derived from the LaSalle hydraulic model study, described on 

p. 3-IV-62 of Exhibit UT-3. Although &he LaSalle study was designed to 
'5-22 

' predict patterns of water withdrawal for the Indian Point plant, LMS 

has argued (Exhibit UT-3, p. 3-IV-62) that the results are also 

applicable at other Hudson River power plants. In the absence of 

hydraulic model studies for Lovett, Roseton, and Danskamner, we, like 

LMS, have applied the Indian Point results to all other plants. 

Our fourth assumption, that all of the water in the upper layer of 

an intake bay is drawn from the upper layer of the river, while water 

in the lower layer of an intake bay is drawn from both layers of the 

river, is not testable with any existing data. It is employed because 

it is the simplest assumption that is consistent with the vertical 

velocity profiles observed at power plant intakes. If 57% of power 

plant cooling water is drawn from the upper layer of the river and 43% 

is drawn from the lower layer, then it must be true that either 57% of 

the total flow enters the plant through the upper half of the intake 

bay, or else water from the upper layer of the river enters the lower 

half of the intake bay. If 57% of the total flow is drawn through the 

upper half of the intake bay, then the mean velocity of water in the . 

upper half should be 1.33 (0.57/0.43) times as high as the mean velocity 

in the lower half. Table 9.1A-4 of Exhibit UT-7A shows that at Bowline 

the ratio of velocities of water entering the upper and lower halves of 

the intake bay is much too small to result in.57% of the total flow 

being drawn in through the upper half. The greatest difference in 

velocities is observed at law tide when all six pumps are operating. 

In this case the ratio of surface to bottom velocities is 1.17 

(0.83/0.71). However, the average velocities in the upper and lower 

layers 'are probably better estimated, respectively, by the mean of the 

surface and middepth velocities and the mean of the middepth and bottom 

velocities. If these latter mea~uresof velocity are compared, then 

the mean velocity in the upper layer of the intake bay is only 1.08 

(0.785/0.725) times as high as that in the lower layer. 

Texas Instruments measured velocity profiles at Indian Point unit 2 

travelling screens in 1974 (Exhibit EPA-92B). In 10 out of 16 

experiments, no statistically significant differences between the 

ve Loc it ies measured at different depths were detected. Approach 
v- 23 

velocities measured on the upper halves of intake screens were 

substantially higher than those measured on the lower halves only in 

four cases in which screens were fitted with experimental fish 

collection baskets (Exhibit EPA-gZB, Table 111-6). No consistent 

vertical trends in velocity were observed in experiments conducted at 

unbasketed screens (Exhibit EPA-92B, Table 111-7). 

Thus,. vertical velocity profiles observed at Bowline and Indian 

Point indicate that when differences between velocities in the upper 

layer and the lower layer of an intake bay exist, the velocity in the 

upper layer is usually higher than in the lower layer. Hawever, euch 

differences are not found consistently and are not large enough to 

result in 57% of the intake flow being drawn in through the upper half 

of the intake bay. Therefore, we feel safe in assuming that erne water 

from the upper layer of the river is present in the lover layer of an 

intake bay. 

Given that some water from the upper layer of the river enters the 

lower half of an intake bay, the simplest possible way to apportion.the 

water coming from the upper and lower layers of the river is to aesume 

that : 

(1) 50% of the total cooling water flaw enters the upper half of 

the intake bay and 50% enters the lover half. 

(2) The 43% of the cooling water flow that is drawn from the 

lower layer of the river all enters the lower half of the 

intake bay. 

Given these two assumptions, the remaining water entering the plant 

through the lower half of the intake bay, representing 7% of the total 

flow, is dram from the upper layer of the river. Therefore 86% 

(43/50) of the water in the lower layer of the intake bay ie drawn frpm 

the lawer layer of the river and 14% is drawn from the upper layer. 

The remaining 50% of the total flow, all dram from the upper layer of 

the river, enters the upper half of the intake bay. 
V- 24 

2.2.2 Description of Computational Procedure 

The first step in the procedure for calculating a W-factor for a 

particular fish population and life-stage at a particular plant is the 

estimation of seasonal upper and lower layer w-ratios. Like LMS 

(Exhibit UT-3, Section 3-IV), we use the surface and middepth samples 

from plant or river to estimate the density of organisms in the upper 

layer and the middepth and bottom samples to estimate the density in 

the lower layer. The plant and river upper-and lawer-layer densities 

for each date are computed using the procedure described in Section 2.1 

for the Modified Utility (MU) method: all of the samples collected on 

a given date from a given layer during a given time period (day or 

night) are pooled [Eq. (V-111. 

In order to compute valid upper- and lower-layer w-ratios under 

the assumptions described in Section 2.2.1, the estimated densities of 

organisms in the upper and lower layers of an intake bay should be 

estimates, respectively, of the densities of organisms in cooling water 

drawn from the upper and lower layers of the river. However, the lower 

layer of an intake bay contains water drawn from the upper layer of the 

river. Since the upper and lower layers of the river may contain 

different densities of organisms, the observed density of organisms in 

the lawer layer of an intake bay must be adjusted to account for the 

presence of water drawn from the upper layer of the river. We define: 

dPU = observed density of organisms in water drawn from the 

upper layer of the river, 

d;L = observed density of organisms in the lower layer of the 

intake bay, reflecting the mixing of water drawn from both 

layers of the river, 

dPL = adjusted density of organisms in water drawn from the 

lower layer of the river. 

Since 86% of the water in the lower layer of the intake bay is drawn 

from the lower layer of the river and 14% is drawn from the upper layer 

(Section 2,2.1), we express the relationship between the adjusted 

.. 

--. 

, 'I' 

-~-

-I-

\, 
V- 25 

density in the lower layer of the intake bay and the observed upperand lower-layer densities using the following equation: 

(V-6) 

Rearranging, the adjusted density of organisms in water drawn from the 

lower layer of the river is given by: 

Equation (V-7) is used to adjust the density estimates for the lower 

' layer of the intake bay as computed from the raw data, to account for 

the presence of water drawn from the upper layer of the river. 

Occasionally, the observed density in the lower layer of the intake bay 

is less than 14% of the observed density in the upper layer. In these 

cases the adjusted density in the lower layer is set equal to 0. 

After the computation and adjustment of density estimates for each 

date, seasonal plant and river upper and lower layer densities are 

calculated as unweighted averages over all dates (i.e., the same 

procedure used in the MU method). Next, w-ratios are obtained by 

computing the ratio of the seasonal plant upper layer density to the 

seasonal river upper layer density and of the plant lower layer density 

to the river lower layer density: 

We next use the gear bias cancelling principle to compute an estimate 

of wL which if free from gear bias. First, we form a ratio of 

w-ratios that can be used to express the observed value of wL as a 

function of the observed value of wU: 

(wu - w,)/(w, + w,) = A . (v-10) 
V- 26 

This particular ratio, rather than other possible ratios (e.g., w /w ), UL 

is employed because the distribution of possible values of A is 

symmetrical around 0 and boilnded by 1.0 and -1.0. If the distribution 

were asymnetrical, then sampling error could cause estimates of A to be 

severely biased. It is easy to verify that gear bias terms cancel out 

of Eq. (V-10) so that the factor A is free of gear bias. Using the 

definitions of wU and wL presented on p. V-18, 

Eq. (V-10) can be rewritten as: 

where DpU 9 DW’ DPL, and DRL are the actual densities of 

organisms in the upper and lower layers of the river and intake bay. 

Next, we express wL as a function of wU and A: 

(v-11) 

As discussed in Section 2.2.1, we assume that there are no lateral 

trends in ichthyoplankton abundance in the upper layer of the river, 

and no active avoidance of the intake structure by entrainable 

organisms. Given those assumptions, and given that all water in the 

upper layer of an intake bay is drawn from the upper layer of the river, 

the density of organisms in the upper layer of an intake bay should be 

equal to the density in the upper layer of the* river. Therefore, the 

actual value of wu, which we will denote as wu, should be equal to 

1.0 (observed deviations from 1.0 are assumed to be attributable to 

sampling error or to unequal *plant and river gear efficiencies). 

Assuming that the value of wv is 1.0, our estimate of the true value 

of wL is given by: 

(v-12) 
V- 27 

Values of A are computed from the seasonal estimates of upper and 

lower layer w-ratios [Eqs. (V-8 and (V-911. Wherever possible, we have 

chosen to combine estimates of A obtained at the same plant during * 

different years before computing wLo We do this for two reasons: 

(1) If the observed value of wu is 0.0, as occasionally occurs 

for some species and life-stages, then unless the*

observed 

value of wL is also 0.0, A is equal to -1.0 and wL 

(= 2/01 cannot be computed. Combining values of A across 

years eliminates this problem. If so few organisms are 

collected at a given plant that observed values of wu are 

equal to 0.0 for more than one year, the GBC method is not 

used to calculate a W-factor . (2) Combining values of A across years allows W-factors to be 

computed for years in which the plant data are not usable 

(e.g., Rosetorr in 19741, provided that sufficient river data 

exist for that year and that sufficient plant data exist for 

other years. 

Thus, estimates of A thzt apply across years at any given plant are 

computed as unweighted means of the values computed for each individual 

year (3 years at Indian Point, 2 years for all* other plants). After 

the across-years estimate of A is obtained, wL is calculated using 

Eq. (V-12). * 

Finally, the computed value of wL and the fractional 

distribution of organisms in the upper and lower layers of the river 

are used to compute the W-factor. If power plants withdrew water 

equally from all depths and there were no vertical stratification of 

organisms in the river, then the number of organisms entrained per unit 

time could be estimated from the following equation: 
..." 

v- 28 

where 

E = number of crganisms entrained per unit time, 

Q = water withdrawal rate, 

W = depth-averaged W-fac tor 

N = number of organisms in the river segment from which the plant 

withdraws water, 

V = volume of the river segment from which water is withdrawn. 

Since we have assumed that the river consists of two distinct layers 

and that power plants draw water differentially from these two layers, 

Eq. (V-13) must be modified to account for these assumptions: 

where 

Qu, QL = water withdrawal rates from the upper and lower layers of 

the river, 

** 

"ut wL = upper and lower layer w-ratios, 

NU, NL numbers of organisms in the upper and lower layers of 

the segment from which water is withdrawn, 

Vvy VL volumes of upper and lower layers of the river 

segment from which water is withdraw;. 

-'_ 

Because we have defined the upper' and lover layers of the river with 

respect to a boundary that is equidistant between surface and bottom 

from shore to shore, Vu is equal to VL. Given that 57% of the 

cooling water is drawn from*the upper layer of the river and 43% from 

the lcwer layer, and that wu is equal to 1.0, Eq. (V-14) can be 

rewritten as: 

r-
V-29 

If we define f U as the fraction of organisms in the river segment 

that are contained in the upper layer of the river and f L as the 

fraction contained in the lower layer, then: 

* 

E = 1.14QfUN/V + 0.86Qw,fLN/V 

(V-16) * = Q(1.14fU + 0.86wLfL)N/V . 

If Eqs. (V-13) and (V-16) are compared, it can be seen that the 

expression in parentheses in Eq. (V-16) is equivalent to the 

depth-averaged W-factor in Eq. (V-13). Therefore, 

w 1.14fU + O.86wLfL * (V-17)

Since we have defined the upper and lower layers of the river so 

that their volumes are equal, only the densities of organisms in the 

upper and lower layers, rather than absolute standing crops, are 

requited in order to estimate f

U and fL. These values are obtained 

from the same data bases used to compute the w-ratios and the MU 

W-factors. Seasonal upper and lower layer densities are computed by 

the same method used in computing the w-ratios [ Eqs. (V-2) and (V-311 

However, data obtained on a11 river sampling dates are used, regardless 

of whether or not simultaneous collections were made at the plant. 

Given the seasonal mean densities DRU and DRL, the fractional 

distribution of organisms in the upper and lower layers of the river is 

calculated from the following equations: 

(v-19) 

Unlike the values of A, f U and f L are computed separately for each 

year. Thus, each GBC W-factor is computed from a value of A that 

applies across years and values of f U and f L that are year-specific. 
r_ 

V-30 

3. RESULTS 

The results obtained when the MU and GBC methods were applied to 

plant and river ichthyoplankton data collected at the Bowline, Lovett, 

Indian Point, Roseton, and Danskarmner power plants are presented in 

Tables V-3 - V-12. The populations for which W-factors could be 

computed are striped bass (Tables V-3 and V-41, white perch (Tables V-5 

and V-61, Atlantic tomcod (Tables V-7 and V-81, bay anchovy (Tables V-9 

and V-101, and -Alosa spp. (Tables V-11 and V-12). Appendix B contains 

descriptions of the data bases used to compute W-factors for each plant 

and year. Separate day and night W-factors were computed for each of 

four lif e-stages (eggs , yolk-sac larvae, post yolk-sac larvae, and 

juveniles) for the years 1974 through 1976. 

It was not possible to compute W-factors for all populations, 

life-stages, plants, and years. In some cases no data were available. 

No 1974 data for any population were available for Lovett and 

Danskamner. Although 1974 ichthyoplankton abundance data were available 

for all species for Bowline and Roseton, only the striped bass data 

were broken down by life-stage. For all other populations yolk-sac 

larvae, post yolk-sac larvae, and juveniles were pooled. W-factors 

could not be computed for those populations and life-stages. For bay 

anchovy, 1976 Lovett intake data were not available. 

In other cases W-factors were not computed because the available 

data were deemed unsuitable (see Section 4) or because too few organisms 

were caught. The 1974 striped bass data collected at the Roseton intake ' 

were excluded because of insufficient sampling effort (see discussion 

in Section 4). Therefore, no MU W-factors could be calculated. Since 

sufficient Roseton/Danskauaner river transect data were available 

for 1974, CBC W-factors could still be calculated. As mentioned in 

Section 2.2, GBC W-factors were not calculated for any population at 

Bowline because this plant does not draw water directly from the river. 

Mu W-factors for Bowline were calculated using the Bowline Pond data 

rather than the intake data to estimate tire density of organisms at the 

plant. Unlike the intake samples, the pond samples are collected using 

J 

,' 

I -

,-
-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

V- 31 

Table V-3. W-factors for striped bass computed using the Modified 

Utility (W) methodasb 

1974 

P1ant Life-stage Day Night 

Bowline Eggs

Y SL 

PYSL 

Juveniles 

Lovett Eggs

YSL 

PYSL 

Juven i1es 

Indian. Eggs Poi nt Y SL 

PYSL 

Juven i1es 

Roseton Eggs

YSL 

PYSL 

Juveni 1es 

Danskamner Eggs

YSL 

PYSL 

Juven i 1es 

aSources of data described in Appendix B. 

1975 1976 

Day Night Day Night 

0.03 0.09 0.28 0.00 

0.32 0.11 0.10 0.17 

0.24 0.98 0.10 0.60 

0.98 0.16 0.28 

4.16 0.43 0.14 0.55 

1.42 3.31 0.04 0.64 

0.64 1.94 0.49 0.85 

0.41 0.28 0.48 0.49 

0.69 1-40 1.07 0.39 

c1.95 2.01 3.31 

1.66 1.03 1.05 0.76 

2.20 0.61 0.91 0.29 

0.39 1.56 6.82 

0-61 0.48 2.58 0.78 

1.23 0.16 2.34 1.03 

bThe available data were not sufficient to calculate W-factors for all 

life-stages at all plants during all years. 
-- 

-- 

-- 

-- 

-- 

-- -- 

-- -- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

V-32 

Table V-4. W-factors for striped bas computed using the Gear Bias 

Cancelling (GBC) rnethoda~g~C 

1974 1975 1976 

P1ant Life-stage Day Night Day Night Day Night 

Lovett Eggs -.I 

1.15 

0.32 0.59 0.32 

YSL -.. 1.16 1.15 1.16 

PYSL -.I 3.37 1.45 3.29 1.55

Juveniles -.c 

Indian 

Point EggsYSL 

1.22 

0.82 

0.16 

0.80 

1.20 

0.77 

0.34 

0.80 

1.20 

0.79 

0.24 

0.86 

PYSL

Juven i 1es 

0.98 

I0.97 0.98 

-0 

0.91

-_ 

0.97 0.91 

0-

Roseton Eggs

YSL 

0.66 

0.98 

0.37 

0.78 

0.37 

0.95 0.78 

0.41 

0.94 

0.24 

0.76 

PY SL 0.55 0.84 0.64 0.88 0.62 0.86

Juven i 1 es 

Danskamner Eggs 1.18 1-17 0.94 

YSL -* 0.55 0.88 0.52 0.86 

PY SL -0 0.69 0.99 0.68 0.99 

Juven i 1es -0 

aSources of data described in Appendix B. 

bThe available data were not sufficient to czlculate W-factors for all 

life-stages at all plants during all years. 

CGBC W-factors are not calculated for Bowline. 

r-
-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

v-33 

Table V-5. W-factors for white perch computed using the Modified 

Utility (MU) methodasb 

Plant Lif e-stage 

Bow1ine Eggs

YSL 

PYSL 

Juven i1es 

Lovett Eggs

Y SL 

PYSL 

Juven i1es 

Indian Eggs Point YSL 

PYSL 

Juven i19s 

Roseton Eggs

YSL 

PYSL 

Juven i1es 

Danskamer Eggs

YSL 

PYSL 

Juven i1es 

1974 1975 1976 

Day Night Day Night Day Night 

21.51 2.04 c0.78 0.49 .­

0.09 0.74 0.24 0.78 

0.67 0.37 

0.21 0.14 

1.12 1.17 0.03 0.13 

2.59 2.19 6.21 

0.81 0.16 1.30 0.14 

0.60 0.50 1.28 1.10 -0 

4.38 3.23 9.56 4.93 

0.85 1.34 0.22 0.56 

0. a4 0.88 1.98 0.40 

16.68 11.14 18.67 16.68 

0.51 0.00 0.21 0.32 

1.85 1.04 0.45 0.83 

aSources of data described in Appendix B. 

bThe available data were not sufficient to calculate W-factors for all 

life-stages at all plants during all years. 
-- -- 

-- 

-- 

-- -- 

-- 

-- -- 

-- -- 

v- 34 

Table V-6. W-factors for white perch computed using the Gear Bias 

Cancel1ing (GBC) methodasb9c 

1974 1975 1976 

P1ant Life-stage Day Night Day Night Day Night 

Lovett Eggs 0.45 1.12 

YSL _- 0.75 0.73 

PYSL 1.12 0.98 1.13 0.98 

Juveni1es -I 

Indian Eggs 1.18 1.43 1.18 1.73 

Point YSL 0.89 0.53 0.93 0.69 

PYSL 0.90 0.76 0.93 0.80 

Juveni1es 

Roseton Eggs 0.43 0.56 0.50 0.54 

Y SL 0.28 1.46 0.64 1.33 

PYSL 1.05 0.99 1.05 0.99 

Juveni1es -0 

Danskamner Eggs 0.35 0.44 0.43 0.53 

YSL 0.69 1.06 0.88 1.09 

PYSL 0.82 1.01 0.80 1.02 

Juveni1es 

IJ 

aSources of data described in Appendix B. 

bThe available data were not sufficient to calculate 1-factors for all 

life-stages at all plants during all years. 

CGBC W-factors are not calculated for Bowline. . 
-- -- 

-- -- 

-- 

v-35 

Table V-7. W-factors for Atlantic tomcod computed using the Modified 

Utility (MU) methodasb 

1974 1975 1976 

P1ant Life-stage Day Night Day Night Day Night 

Bowline E9gs

YSL 

PYSL 

Juveni1es 

Lovett Eggs

YSL 

PYSL 

Juveni 1es 

Indian Eggs Poi nt YSL 

PYSL 

Juveni I es 

Roseton . Eggs

YSL 

PYSL 

Juveni12s 

Danskamner Egg5 

Y SL 

PYSL 

Juveni1es 

0.40 0.50 

0.41 1.09 

0.00 0.00 

0.42 0.71 

1.27 1.70 

0.00 0.04 

_­

0.70 10.34 

aSources of data described in Appendix B. 

I,- . bThe available data were not sufficient to calculate W-factors for all 

life-stages at all plants during all years. 
-- -- 

-- 

-- -- 

-- -- 

-- 

-- 

V-36 

Table V-8. W-factors for Atlantic tomcod computed using the Gear Bias 

Cancel1ing. (GBC) rnethoda9bsc 

P1 ant Life-stage 

Lovett Eggs

YSL 

PYSL 

Juven i1es 

Indian Eggs Point YSL 

PY SL 

Juven i1es 

Roseton Eggs

YSL 

PYSL 

Juven i1es 

Danskamner Eggs

YSL 

PY SL 

Juven i1es 

1974 1975 

Day Night Day Night 

0­

1.32 1.03 

1,08 0.66 

0.08 0.10 

-0 .. e­

-I 

-0 0­

0.32 0.57 0.42 0.55 

1976 

Day Night 

-. 

1.28 1.00 

1.08 0.53 

0.08 0.08 

I­

-0 

2.04 0.54 

” -.. 

% / 

~ 

aSources of data described in Appendix 8. 

bThe available data were not sufficient to calculate W-factors for all 

life-stages at all plants during all years. 

CGBC W-factors are not calculated for Bowline. 

..i 
-- 

-- 

-- 

-- 

-- 

-- -- 

-- 

v-37 

Table V-9. W-factors for bay anc ovy computed using the Modified Uti1ity (W) methodasI: 

P1ant Life-stage 

Bowline Eggs

YSL 

PYSL 

Juveniles 

Lovett Eggs

YSL 

PYSL 

Juveni1es 

Indian Eggs Point YSL 

PYSL 

Juveni1es 

Roseton Eggs

YSL 

PYSL 

Juveni1es 

Danskamner Eggs

YSL 

PYSL 

Juveni 1es 

1974 1975 1976 

Day Night Day Night Day Night 

0.11 0.31 0.01 0.57 

0.15 

0.95 0.98 0.24 0.51 

0.78 1.77 

7.11 4.31 

44.35 225.35 

1.97 3.65 

0.17 

0.45 0.81 1.26 0.90 2.24 9.19 

1.65 1.39 0.12 0.67 0.41 

1.45 0.63 0.97 1.60 2.03 0.20 

1.27 36.32 1.61 10.53 1.05 

aSources of data described in Appendix B. 

bThe available data were not sufficient to calculate W-factors for all 

life-stages at all plants during all years. 
-- 

-- 

-- 

Table V-10. W-factors for bay anchovy computed using the Gear Bias 

Cancel1ing (GBC) met hodasbJ 

1974 1975 1976 

P1 ant life-stage Day Night Day Night Day Night 

Lovett EW 

YSL 

0.38 0.49 

0.06 156.94 

PYX 1.34 1.06 

Juven i1es 7.83 1.20 

Indian Eggs 0.53 0.33 0.53 0.19 0.60 0.16 

Point YSL 1.43 1.35 0.30 1.40 0.27 

PYSL 1.01 0.58 0.96 0.63 0.98 0.60 

Juven i1es 0.31 0.74 0.68 0.85 0.86 

Roseton Eggs

YSL -e 

PYSL 

Juveni1es ., 

" 7 Danskamner Eggs

YSL 

.PYSL 

Juven i1es 

aSources of data described in Appendix 6. 

bThe available data were not sufficient. to calculate W-factors for all 

life-stages at all plants during all years. 

CGBC W-factors are not calculated for Bowline. 
-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- -- -- 

-- 

-- 

-- 

v-39 

Table V-11. W-factors for AlGsa s p. computed using the Modified Utility (MU) rnZFiZasg 

Plant Lif e-stage 

PYSL 

Juven i 1es 

Lovett Eggs

YSL 

PYSL .. Juven i1es 

Indian Eggs Point YSL 

PYX 

Juveniles 

Roseton Eggs

YSL 

PYSL 

Juven i1es 

Danskamner Eggs 

YSL 

PYSL 

Juven i1es 

~ ~~ ~~ ~ ~~~~ ~-­

1974 1975 1976 

Day Night Day Night Day Night 

-I -I 

0.44 5.33 

-0 

0.00 -e 

2.26 1.65 . 

0.79 0.50 1.28 1.49 

1.36 1.47 1.05 1.82 0- -.I -0 

12.19 1.53 10.07 5.82 

1.18 0.91 0,63 0.96 

0.55 9.40 0.34 0.66 

1.33 0.43 1.97 

62.24 22 ,23 17.77 52.67 

2.07 1.32 0.46 0.76 

1.12 0.40 0.53 0.69 

0.71 0.17 0.38 

aSources of data described in Appendix 6. 

bThe available data were not sufficient to calculate W-factors for all 

life-stages at all plants during all years. 
-- 

--- 

V-40 

Table V-12. W-factors for Alosa spp. computed using the Gear Bias 

Cancel 1i ng (G6~thoda~b~C 

1974 1975 1976 

PIant Life-stage Day Night Day Night Day Night 

Lovett EXIS 

YSL 

PYSL 

Juven i1es 

Indian

Point Eggs

YSL 

-- -- 0.86 0.69 

-- -- 0.89 0.59 

-_ -- 0.92 0.69 

PY SL 

Juveni 1es 

1.02 0.91 -- -- 1.06 0.90 -- -- 1.05 0.89 -- --

Roseton Eggs

YSL 

0.48 0.77 

0.78 0.84 

0.51 0.96 

0.87 0.78 

PYSl 0.92 0.88 0.93 0.88 

Juven 5 1es 0.53 0.85 

Dansk anmer Eggs

YSL 

0.47 0.89 

0.57 0.74 

0.50 1.02 

0.70 0.66 

PYSL 1.16 0.98 1.16 0.97 

Juven i1es 0.92 1.23 

aSources of data described in Appendix B. 

bThe available data were not sufficient to‘ calculate W-factors for all 

life-stages at all plants during all years, 

CGBC W-factors are not calculated for Bowline. 

r-. 

,d 

”... 

r­

.. 

, 
V-41 

the same gear and deployment technique used to collect the river 

samples. Therefore, MU W-factors computed from the pond data should be 

free from gear bias. 

W-factors computed using any method will be highly unreliable if 

they are based on ichthyoplankton collections containing only a few 

organisms. Unrealistically high or low W-factors (particularly values 

of 0 or infinity) are more likely to be produced under these 

circumstances. Therefore, we used an exclusion criterion to eliminate 

W-factors based on insufficient numbers of organisms. In order to 

calculate a W-factor for a given population, life-stage, and period 

(day or night) at a given plant, we require that at least 10 organisms 

have been collected in either the plant samples or the river samples 

used to compute that W-factor. Many data sets failed to meet the 

10-organisms exclusion criterion. In particular, in only a few cases, 

primarily for bay anchovy and Alosa, was it possible to compute 

W-factors for entrainable juveniles. So few data sets for juvenile 

striped bass and white perch satisfied the 10-organism criterion that 

we decided not to compute any MU or GBC W-factors for juveniles of 

these species. 

When near-field data collected at parer plant intakes and river 

transects were unavailable or unusable (Section 41, W-factors were 

computed from riverwide abundance data using the method described in 

Chapter V-6. 
V-42 

4. DIFAGREEMENTS WITH DATA AND METHODS USED BY LMS 

IN EXHIBIT UT-3 TO COMPUTE W-RATIOS 

Ideally, plant river samples used to compute w-ratios or W-factors 

should be collected using identical gear deployed in an identical manner 

(Carpenter 1979). Moreover, large numbers of samples should be 

collected at each location in order to obtain estimates of plant and 

river abundance that are as precise as possible given the limitations 

imposed by the patchy distribution of ichthyoplankton. Judged by these 

criteria, none of the Hudson River sampling programs has every been 

ideal for obtaining data suitable for estimating W-factors. For the 

most part we have used the available data in spite of their limitations. 

However, some of the data have been collected under conditions that 

deviate so far from the ideal that we believe they should not be used 

to compute W-factors. In this section we identify these data and 

explain our reasons for excluding them. We also discuss two 

methodological problems that we believe have led LMS to underestimate 

the numbers of organisms entrained at Hudson River power plants. One 

of these is the overestimation of sample volumes at the Indian Point 

intake and discharge staions. The other is LMS' assumption that if no 

organisms are collected in either the plant or the river samples, the 

w-ratio is equal to zero. 

4.1 NINETY MINUTE SAMPLE DURATIONS AT THE BOWLINE INTAKE 

According to page 9.1A-15 of Exhibit UT-7A, during the entire 1974 

entrainment sampling season and prior to June 10, 1975, the sampling 

duration at the Bowline intake was 90 minutes. Ecological Analysts 

(l976a) conducted studies at Bowline and Lovett between May and July 

1975 in order to determine whether nets left in the water for such 

'extended periods 0.f time could become clogged with detritus. It was 

found that such clogging could indeed occur, and could result in 

substantial reductions in the filtration efficiency of the nets. 

Sample volumes at Bowline are not measured directly from flowmeter 

readings, but are estimated indirectly using the matrix of intake 
v-43 

velocities presented in Table 9.1A-4 of Exhibit UT-7A. Therefore, in 

addition to possible increased gear avoidance, reductions in f ilttation 

efficiency due to clogging would cause sample volumes at the Bowline 

intake to be overestimated. If intake sample volumes used in 

computations are erroneously high, then the densities of organisms in 

the intake samples (and therefore the w-ratios) would be erroneously 

low. 

According to EA's 1975 Annual Interpretive Report for Bowline 

(Ecological Analysts 1976a, p. B-21, significant reductions in net 

filtration efficiency due to clogging were observed in experiments 

conducted on May 7, May 14, June 2, June 25, and July 10, 1975. 

Clogging was most frequently observed in nets placed at the bottom of 

the intake bay. In some of these experiments, clogging began to be 

observed after only about 40 m3 of water had been filtered; in all 

experiments in which significant clogging was observed,' it was observed 

before 80 m 

3 had been filtered. The importance of this finding can 

be seen from Table V-13, which contains inforatation pertaining to the 

volumes of samples collected at the Bowline intake during the period 

between June 5 and July 12, 1974 (no intake samples collected prior to 

June 5 were used to compute w-ratios). The volumes of intake samples 

collected during this period, as estimated using Table 9.1A-4 of . Exhibit UT-'IA, are far higher than the 40 to 80 m' range in which EA 

began to observe clogging. The mean sample volumes on the four dates 

listed in Table V-13 ranged from 132 to 148 m 

3 . The volumes of the 

3 smallest samples collected during this period were larger than 130 m . Based on EA's observations of clogging zt sample volumes of 80 m 

3 and 

less, such clogging could have occurred frequently at the Bowline intake 

in 1974. According to the testimony of Dr. Gerald Lauer of EA the 

clogging of nets observed at Bowline in 1975 was an unusual occurrence 

related to "an extraordinary increase in the volume of detritus 

collected in the nets" (Transcript pp. 8119). Under cross-examination 

he claimed that this conclusion was included in EA'S report. Dr. Lauer 

stated (Transcript pp. 8119-20): "as I recall from the report itself, 

in general the report concluded that based upon these perceptions of 
v-44 

Table V-13. Mean, minimum, and maximum volumes of samples collected at the Bo line intake on selected sampling dates during 1974a~1 

~~ 

Volume (m3) 

Date Mean (number of samples) Minimum Maximum 

June 5 (day) 142.5 (12) 131.4 163.8 

June 5 (night) 132.3 (12) 131.4 134.1 

June 12 (day) 136.1 (9) 131.4 163.8 

June 12 (night) 142.5 (6) 131.4 163.8 

June 19 (day) 145.9 (18) 131.4 163.8 

June 1-9 (night) 137.4 (12) 131.4 163.8 

June 26 (day) 142.5 (18) 131.4 163.8 

June 26 (night) 147.6 (12) 131.4 163.8 

July 2 (day) 142.5 (12) 131.4 163.8 

July 2 (night) 140.6 (11) 131.4 16318 

~~ - ~~ ~ ~ ___ ~ -

aSource of data described in Appendix B. 

bData collected on these dates were used by LMS to compute w-ratios for striped bass life-stages. 
v-45 

volume of detritus that you see in the nets ordinarily, this was an 

extraordinary occurrence that you would not expect to see most of the 

time in most years." Despite a thorough search, I found no such 

conclusions anywhere in EA'S report on the clogging experiments 

(Ecological Analysts 1976a, pp. B-1 - B-9). Moreover, Ecological 

Analysts did not conduct the entrainment sampling at Bowline in 1974 

(it was done by LMS), so that neither Dr. Lauer nor any other EA 

personnel could have observed whether detritus loads at the Bowline 

plant were lower in 1974 than in 1975. 

In any case, Dr. Lauer's suggestion that the occurrence of clogging 

can be determined from the amount of visible detritus collected in the 

nets is at variance with the conclusion of Smith, Counts and Clutter 

(1968; p. 245) that: 

We found that indirect means of judging whether clogging 

will take place, such as taking a Secchi disc reading, was 

not a suitable substitute for monitoring filtration 

efficiency. Inspection of the net after a tow was not 

reliable for determining whether it had clogged. After heavy 

clogging at San Pedro, the net was discolored but no 

indication of clogging appeared at the Catalina Island site, 

even when the net had been accepting less than half of the 

water presented to it at the end of the tow. 

According these authors, clogging can be detected only by comparing 

velocity readings taken from flcwmeters placed inside and outside a 

plankton net. 

Since there is no scientifically valid evidence that the amount of 

detritus present at the Bowline plant was higher in 1975 than in 1974, 

and since subjective impressions about the amount of detritus collected 

in a plankton sample are not alone sufficient to determine whether or 

not significant clogging has occurred, we conclude that clogging may 

well have occurred during the 1974 sampling program and gone undetected. 

Therefore we believe that all of the data collected at the Bowline 

intake during 1974 and up to June 10, 1975 should have been excluded 

when LMS' w-ratios were calculated. We have not used any'of this data 

to compute W-factors. The sampling program conducted at Lovett in 1975 
V-46 

was identical to that at Bowline in that the duration of sampling at _-

the intake was 90 minutes on all sampling dates prior to 3une 10, 1975 

(Ecological Analysts 1976b). We have excluded this data from our 

estimates of W-factors for all species except Atlantic tomcod. This 

exception was made because Lovett and Bowline are the only plants at 

which enough simultaneous plant/river sampling is conducted during the 

early spring for W-factors to be calculated for yolk-sac larvae of this 

species. We felt that the advantage of having the additional data for 

this life-stage outweighed the disadvantage of the potential bias. 

4.2 BOWLINE POND VS BOWLINE INTAKE 

When the Bowline plant is operating, Bowline Pond functions as a 

very large intake bay: water is pulled into the pond by the pumps, 

entrained through the plant, and discharged back out into Baverstraw 

Bay. Organisms drawn in with the cooling water can leave the pond in 

only two ways: by being entrained through the plant or by swimming 

back out of the pond against the current caused by the intake flow. In 

order to avoid being entrained, an organism must first locate the pond 

inlet, a "keyhole" only 240 feet Wide and about 15 feet deep (Exhibit. 

UT-!, Fig. 3.1-6). Judging from Fig. 3.1-7 of Exhibit UT-7, the pond 

itself is roughly 1500 to 2000 feet across and 30 to 40 feet deep. 

Having reached the inlet, the organism must then swim out against the 

flow being drawn into the pond. 

It does not seem likely that very many larvae could achieve this 

feat before being entrained (escape is, of course, out of the question 

for eggs). If all, or nearly all, the ichthyoplankton drawn into 

Bowline Pond is destined to be entrained through the plant, it seems 

reasonable to us to use the data collected in Bowline Pond, rather than 

data collected at the intake, to calculate W-factors. As we mentioned 

in Section 2.2 and again in Section 3, the pond samples are collected 

with the same gear used to collect the river samples, and thus no gear 

biases should be introduced when w-ratios are calculated using pond 

data rather than intake data. LMS did calculate w-ratios using pond 
v-4 7 

data, but only when no intake data was available. We believe that the 

pond data are clearly superior to the intake data and that they should 

have been used to calculate all of the w-ratios for Bowline. 

4.3 INSUFFICIENT SAMPLING EFFORT AT THE ROSETON PLANT, 1974 

The sampling program conducted at Roseton in 1974 was of 

especially poor design for estimating W-factors. Only 12 samples were 

collected at the intake on each sampling date. Of these, six were 

collected at dawn or dusk. However, LMS did not use dawn or dusk data 

to compute w-ratios. Thus, half of the samples collected at the 

Roseton intake were automatically excluded from LMS' analysis. Of the 

remaining six samples per date, many failed to meet LMS' outlier 

criteria (Exhibit UT-3, p. 3-IV-49) and were therefore excluded. The 

remaining sampling effort, measured either as the number of samples 

collected or as the total volume sampled, was extraordinarily small. 

Table V-14 contains the total number of day and night samples collected 

(minus outliers) and the total day and night sample volumes for five 

sampling dates between May 9 and July 2, 1974. Data collected on these 

five dates were used by LMS to compute all 1974 w-ratios for Roseton 

(Exhibit UT-3, p. 3-IV-51). After excluding outliers, dawn and dusk 

samples, and samples collected on days when no river samples were 

collected, only 17 samples remain for the entire striped bass 

entrairiment season. Only eight samples were available for computing, 

daytime w-ratios and only nine for nighttime w-ratios. On three of the 

five dates the total volume sampled during the day was less than 

20 m 

3 

; on two dates less than 20 m 

3 

was sampled at night. The 

highest volume sampled, on the night of July 2, was less than 120 m 

3 . By comparison, the smallest individual sample collected at Bowline 

3 between June 5 and July 12, 1974 had a volume greater than 130 m 

(Table V-13). 

Tables V-14 throagh V-16 contain similar analyses of the sampling 

effort at Roseton in 1975 and at Bowline and Indian Point in both 1974 

and 1975. The data used by LMS to compute w-ratios for Bowline and 

Indian Point in 1974, and for all three plants in 1975, were obta'ined 
V-48 

Table V-14. Numbers of samples and total volumes samttled at the 

Roseton intake' on selected sampling dates during 1974 

and 1975a~b 

_-

Volume' (number of samples) 

Date Day Night 

1974 

May 9 10.0 (1) 8.5 (1) 

May 21 69.7 (3) 46.7 (2) 

June 4 0 (0) 15.6 (1) 

June 19 63.5 (3) 71.4 (2) 

July 2 18.8 (1) 117.9 (3) 

1975 

May 15 420.9 (6) 259.7 (3) 

May 22 406.6 (6) 205.5 (3) 

May 29 748.6 (8) 410.6 (5) 

June 2 251.9 (18) 126.9 (8) 

June 9 272.0 (6) 125.7 (3) 

June 19 292.8 (6) 128.6 (3) 

June 23 261.5 (6) 147.5 (3) 

aSource of data described in Appendix B. 

bData collected on these dates were used by LMS to compute w-ratios for striped bass life-stages. 

CSum of volumes of all samples collected during the day or night,

after exc1ud i ng out 1iers . 
v- 49 

Table V-15. Numbers of samples and total volumes sampled at the Indian 

Point intake and discharge stations on selected sampling dates during 1974 and 1975a~b9c 

Date 

May 7 

May 28 

June 4 

June 13 

June 25 

July 2 

July 9 . 

1974 

Vo1ume 

(number of samples) Date 

279.9 (23) 

258.5 (18) 

519.6 (36 

425.0 (27) 

473.2 (34) 

663.6 (43) 

476.0 (33) 

May 13 

M3y 20 

May 27 

June 3 

June 10 

June 17 

June 24 

July 1 

1975 

Vo 1me 

(number of samples) 

179.8 (22) 

545.5 (92) 

470.0 (72) 

448.1 (72) 

379.5 (54) 

608.6 (96) 

869.1 (140)

555.8 (88) 

aSource of data described in Appendix 6. 

bData collected on these dates were used by LMS to compute w-ratios 

for striped bass 1ife-stages. 

CAll intake and discharge stations combined. 

dSum of volumes of all samples and total number of samples ccllected 

during nighttime at all intake and discharge stations (no plant samples 

are collected during daytime), after excluding outliers. 
V-50 ,.~". 

Table V-16. Numbers of samples and total volumes sampled at the Bowline 

intake and in Bowline Pond on selected sampling dates 

during 1974 and 1975asb 

Yo1ume (number of samp1es ) _-

Bowline intake Bowl ine pond 

Date Day Night DaY Night -. 

-

May 8 0 (0) 0 (0) 1425.5 (6) 1572.7 (10) 

May 22 0 (0) 0 (0) 1225.8 (8) 2048.7 (10) 

June 5 1710.0 (12) 1587.6 (12) 1185.1 (7) 2261.9 (10) 

June 19 2626.2 (18) 1648.8 (12) 1285.3 (8) 1758.8 (10) 

' June 26 2565.0 (18) 1771.2 (12) 217.9 (4) 0 (0) 

July 2 1710.0 (12) 1546.2 (11) 1946.2 (10) 752.8 (3) 

1975 

May 20 0 (0) 0 (0) 510.1 (2) 558.4 (2) 

May 27 0 (0) 0 (0) 575.0 (2) 602.0 (2) 

June 3 333.9 (3) 683.1 (6) 570.2 (2) 529.0 (2) 

June 10 50.3 (8) 92.7 (15) 586.7 (2) 612.7 (2) 

1974 

-

June 17 92.7 (15) 37.1 (6) 618.5 (2) 563.2 (2) 

June 18 30.9 (5) 117.4 (19) 0 (0) 0 (0) -~~­

aSource of data described in Appendix B. 

r ­ bData collected on these dates were used by LMS to compute w-ratios 

for striped bass life-stages. 

CSum of volumes of all samples and total number of samples collected 

during the day or night, after excluding outliers. 
V-51 

from sampling programs that were far more intensive than that conducted 

at Roseton in 1974. Eighty-four intake samples were used to compute 

the 1975 w-ratios for Roseton (Table V-14). The smallest volume 

sampled at Roseton during the day or night on any date in 1975 

(125.7 m3 on the night of June, 9) was greater than the largest volume 

sampled during any similar period in 1974. 

On each night during both years more samples were collected at the 

Indian Point intake and discharge stations than were used by LMS to 

compute 1974 w-ratios for Roseton (Table V-15). On most of these 

nights the total volume sampled exceeded the total volume (422 m 

3 of 

the 1974 Roseton intake samples. Only at the Bowline intake on June 

10, June 17, and June 18, 1975 were total day or night sample volumes 

as low as the volumes sampled throughout the 1974 entrainment season at 

Roseton (Table V-16). 

In Section 1.1 we discussed the fact that ichthyoplankton 

distributions in the Budson River are patchy, or overdispersed. 

consequence of this over-dispersion, the probability that any 

particular intake sample will contain zero organisms, even when 

organisms are in fact being entrained, is much higher than it would be 

As a 

if organisms were randomly distributed throughout the intake water. 

Therefore, given the low sampling effort, it is not surprising that 

very few striped bass were collected at the Roseton intake in 1974. We 

have determined from LMS' data tapes (Appendix B) that the 17 samples 

used to cmpute the w-ratios for 1974 contained a total of three 

striped bass yolk-sac larvae, all caught in the single sample available 

for June 4, and two post yolk-sac larvae, both caught at night on 

July 2. In our opinion no meaningful estimates of the abundance of 

striped bass ichthyoplankton in water entrained by the Roseton plant 

can be obtained from the data collected at the Roseton intake in 1974. 

No faith can be placed in the w-ratios calculated from these data. We 

have not used them to compute any W-factors. 
4.4 

1974 

V-52 _.. 

USE OF'SAMPLES COLLEClXD AT INDIAN POINT 

RIVER STATIONS F AND G 

NYU collected ichthyoplankton samples at seven river stations in 

and 1975 (Exhibit UT-3, Fig. 3-IV-8). LMS used data from all 

seven stations to compute the densities of striped bass life-stages in 

the. vicinity of the Indian Point plant. These river densities were 

then used to compute the w-ratios. In addition, data collected at 

three of the seven stations, stations B, D, and G, were used as 

substitutes for intake and discharge stations when the daytime w-ratios 

were calculated (no plant samples are collected during the daytime at 

Indian Point 1. 

According to Table 1-5 of NYU's Progress Report for 1975 (New York 

University 19771, Stations A through E are all located between river 

miles 41 and 43 (the plant itself is located at RM 42). Stations F 

and G, however, are located at RM 39, three miles downriver from the 

plant. We believe that stations F and G are too far from the Indian 

Point plant to be considered "near-field" stations and therefore we 

have not used data collected at these stations to compute W-factors. 

According to p. 3-IV-20 of Exhibit UT-3, the hydraulic model 

studies performed at LaSalle Hydraulic Laboratory show that "water 

comes to the Indian Point intakes from a band about 200 to 350 Et (61 to 

107 m) wide along the east shore of the river during the ebb and flood 

stages." At slack tide, water is drawn from 'la radius of approximately 

1600 ft (488 m) from the intake." It is for this reason that we chose 

stations D and E rather than stations C and D as substitutes for intake 

and,discharge stations. Although station C is directly opposite the 

Indian Point intakes (Exhibit UT-3, Fig. 3-1V-8), it is more than 1600 

feet from those intakes, according to Dr. O'Connor of NYU (Transcript 

p. 8527). Since station E is closer to the shoreline than station C 

(Exhibit .UT-3, Fig. 3-IV-81, in theory it should be more representative 

of the water actually entrained by the plant. We say "in theory" 

because in practice no consistent differences have been observed among 

the densities of entrainable striped-bass life stages at any of river 

stations A through E. According to Table 7-5 of NU'S Progress Report 
v- 53 

for 1974 (New York University 19761, in 1974 significantly lower 

densities of striped bass larvae (= post yolk-sac larvae) were observed 

at station G than at station B. Otherwise, no significant differences 

among stations were observed. According to Table 7-6 of NYU's 1975 

Progress Report (New York University 19771, in 1975 no significant 

differences in abundance among stations were observed for any striped 

bass life-stage. These results appear to indicate that, at least for 

striped bass, it does not make a great deal of difference which river 

stations are chosen to represent the plant. 

4.5 INTAKE AND DISCHARGE SAMPLE VOLUMES AT INDIAN POINT 

In 1974 and 1975, the volumes of samples collected at the Indian 

Point intake and discharge stations were not measured with flowmeters. 

Instead, volumes were estimated indirectly, as they are at Bowliue and 

Lovett. However, unlike US, NYU did not use actual measurements of 

intake or discharge canal velocities to estimate the volumes sampled. 

The volumes were calculated assuming that at full flow the intake 

velocity at both Indian Point Units 1 and 2 is 1.0 feet per second, and 

that the velocity at all discharge stations is 4.5 feet per second 

(Exhibit EPA-1411. These velocities were multiplied by a flow factor 

equal to the fraction of the maximum possible flow observed on each 

sampling date (Exhibit EPA-141). The velocities used to compute sample 

volumes are incorrect; NYU's applicaton of the flow factors is also 

incorrect. According to Table 1-2 of NYU's Progress Report for 1975 

(New York University 19771, the maximum design velocities at the Indian 

Point Unit 1 and 2 intakes are, respectively, only 0.7 feet per second 

and 0.9 feet per second. The design velocity at discharge station D-1 

is 4.4 feet per second, only slightly smaller than the assumed value. 

However, the design velocity at discharge station D-2 is substantially 

Smaller, only 3.4 feet per second. The velocities presented in 

Table 1-2 of NYU's 1975 Progress Report are those expected at mean low 

water in the Hudson. According to the heading of Table 1-2, velocities 

are expected to be 10% lower than the stated values at high slack tide 

and 5% lower at low slack tide. 
v-54 

On Transcript pp. 8244-45 Dr. O'Connor of NYU confirmed that the 

design velocities are the velocities that should have been used: 

MR. KURENT: If it were a full flaw situation- that is, 

100 percent operation- would you use the one foot per second 

at the intake and the 4.5 feet per second at the discharge? 

DR. 0'CCN"NR: No. 

According to the design values we had at the time, we _-

would have used .9 feet per second for the Unit 2 intake 

and .7 feet per second for the Unit 1 intake. 

On Transcript pp. 8254-55, Dr. O'Connor stated that, instead of 

the design velocities contained in NYU's 1975 Progress Report, the 

velocities contained in Exhibit EPA-141 were used in the preparation of 

the utilities' testimony for these hearings: -_ 

MR. KURENT: Although Table 72 does reflect less than 

full flow was considered, did you still assume as stated in ?-

Exhibit 141 that full flaw velocity was one foot per second? 

DR. O'CONNOR: Yes, we did at the time of the preparation 

of this report. That value, however, was corrected earlier 

this spring when the data - when the plant operating data 

were being reevaluated in preparation for submission of the 

Federal Energy Regulatory Coxmission report on Cornwall. 

MR. KUREW: Are you saying that you used the one-foot 

per second velocity for the preparation of the testimony that 

was submitted on July 11, 1977 for this hearing? 

DR. O'CONNOR: Add (sic) corrected by the percentage 

flw, yes. 

~

The errors introduced by NYU's assumption of erroneously high intake 

.velocities are partially offset by an erroneous application of flow 

factors to correct for cooling water flows of less than 100% of 

capacity. When any particular circulator pump is operating, that pump 

is operating at 100% of capacity, regardless of whether other pumps are 

operating or not. Since each pump at Indian Point Units 1 and 2 is 

located in a separate forebay, the intake velocity within a forebay 

containing an operating pump should always be 100%of the design flow. 
v-55 

Since an intake sample can be collected only from a forebay containing 

an operating pump, no flow factors need to be applied in order to 

estimate the volumes of the intake samples. There is no such 

offsetting error in the computation of the discharge sample volumes. 

When the actual cooling water flow is less than 100%of capacity, the 

design velocities presented in NYU's 1975 Progress Report for discharge 

Stations D-1 and D-2 must be adjusted downward using the flaw factors. 

Primarily because of the erroneously high values used for sampling 

velocities at the Unit 1 intake and at discharge Station D-2, the 

nighttime densities (and the w-ratios as well) of striped bass life 

stages computed by LMS for the Indian Point plant in 1974 and 1975 are 

erroneously low. As no plant samples are used to compute daytime 

w-ratios, these are not affected by the errors discussed in this secton. 

4.6 COMPARISON OF SURF'ACE AND MIDDEPTX SAPIPUS 

COLLECTED IN BOWLIlJE POND TO SURFACE, MIDDEPTH, 

AND BOITOM SAMPLES COLLECTED W THE RIVER 

During May of both 1974 and 1975, pairs of river and plant data 

sets collected on the same day were not available for Bowline. Since 

paired sets of river and Bowline Pond data were available, LMS 

calculated w-ratios using the pond data as a substitute for data 

collected at the plant itself. We agree that it is valid to use data 

collected in Bowline Pond to calculate W-factors. In fact, as is 

stated in Section 4.2, we believe that these data are superior to data 

collected at the Bowline intake and that they should be used to compute 

all W-factors for Bowline. We disagree, however, with the method used 

by LMS to compute w-ratios using the pond data. 

Two stations in Bowline Pond, referred to as Bowline Pond Long 

(BPL) and Bowline Pond Short (BPS), were sampled in 1974 (Exhibit 

UT-7A, p. 9.1A-7). Only Bowline Pond Long was sampled in 1975 (Exhibit 

UT-7A, p. 9.1A-7). Surface, mid-depth, and bottom samples were 

collected at station BPS, but, due to obstructions on the bottom of the 

pond, only surface and mid-depth samples were collected at station 

BPL. LMS computed pond lower layer densities for 1974 using bottom 
V-56 

samples collected at BPS and mid-depth samples collected at both BPS 

and BPL. Similar lwer Layer densities for 1975 were computed using 

only the mid-depth samples collected at Bowling Pond Long. The 

correspcmding lower layer densities for the river were computed using 

both mid-depth and bottom samples. 

The highest densities of striped bass eggs and larvae in the river 

are generally found near the bottom, especially during the daytime 

(Exhibit UT-4, Sections 7.3.6, 7.4.1.6, and 7.4.2.7). If the pattern 

of distribution of these life-stages in Bowline Pond is similar to that 

found in the river, then the lower layer w-ratios computed by LMS using 

the 1975 pond data may be biased. If eggs and larvae in Bowline Pond 

are more abundant at the bottom than at mid-depth, and if bottom 

samples had been collected at station BPL, then higher lower layer 

densities, and therefore higher lower layer w-ratios, would have been 

computed. 

In the absence of bottom samples, it is not possible to show 

whether the vertical stratification of ichthyoplankton in Bowline Pond 

is similar to or different from that found in the River. However, 

there is no reason to believe that these organisms behave any 

differently in the pond than they do in the river. Eggs will still 

tend to sink unless resuspended by turbulence. Larvae will still 

migrate toward the bottom during the day and disperse throughout the 

water column at night. This fact was acknowledged by Dr. O'Connor of 

NYU on Transcript p. 8548: 

DR. O'CONNOR: As pointed out by Dr. Englert, the larvae 

probably undergo the same sorts of diurnal migration in the 

pond, those that might occur there as they undergo in the 

river . 

However, Dr. O'Connor claimed that the turnover rate of Bowline Pond 

(i.e., the rate at which water in the pond is entrained through the 

Bowline plant and replaced by water drawn from the river) is so high 

that eggs and larvae-would not have the to accumulate at the bottom 

(Transcript pp. 8539-41): 

,.. . 

,-, 
v-57 

MR. KURENT: Would you expect the concentration of 

striped bass eggs to be highest near the bottom given the 

conditions prevailing in that pond? 

DR. O'CONNOR: I would not expect that. 

First of all, there is no evidence that striped bass 

spawn in Bowline pond. Therefore, to have large numbers of 

eggs in the pond is unlikely. 

The entrance to the Bowline pond, I believe, has a depth 

only of five feet. Therefore, any striped bass eggs emerging 

the pond are going to be those from about the top five feet 

of water in the river and the turnover of water in the pond 

is sufficiently rapid; I would believe that there would be 

insufficient time for the eggs to sink to the bottom. 

MR. KURENT: Larvae that would be present in the por-d 

would be more likely to remain at or near the bottom amongst 

the objects near the bottom; wouldn't they? 

DR. O'CONNOR: I honestly couldn't say. 

First of all, there are no samples from the very bottom 

strata. 

Larvae which entered the pond, again, will be those 

which are capable of coming over that sill at the entrance to 

the pond. And since most of the data, to my recollection, 

indicate that yolk sack larvae and the younger of the post 

yolk sack larvae tend to remain in the bottom and middle 

depths of the river, that the numbers coming over the sill 

would be relatively small. 

And, again, turnover in the pond would probably be 

sufficiently rapid to prevent them from forming any great 

accumulation in the bot tom. 

Since there is no information available on the sinking rate of striped 

bass eggs in Bowline Pond, there is no way to verify or disprove Dr. 

O'Connor's claim with respect to this life-stage. However, it seems 

likely that there is in fact sufficient time for the actively migrating 

larvae to move to the bottom. Diurnal migration occurs over a period 

of only a few (at most 12) hours: larvae present in the upper strata 

during the night are capable of moving to the bottom strata by the next 

afternoon. Therefore, unless the average residence time of larvae 

present in the pond is less than 12 hours, they will be able to migrate 

to the bottom before being entrained. 
V-58 

We believe Lhat given the tendency of organisms to distribute 

themselves within the pond in the same way they are distributed in the 

river, and given the lack of data on the actual vertical distribution 

of organisms in the pond, it is better to assume that the pond and 

river distributions are the same. Therefore, in our calculation of 

W-factors for Bowline we have compared data collected at the surface 

and at mid-depth in Bowline Pond to data collected at the same depths 

in the river. We have excluded all of the data collected at the river 

bottom. For consistency we have also excluded the bottom samples 

collected at station BPS in 1974. 

4.7 ZEm DIVIDED BY ZER!3 EQUALS ZERO 

At Bowline in 1974 no striped bass eggs were caught during the 

daytime at either the river or the Bowline Pond stations assigned to 

the lwer layer (Exhibit UT-3, Table 3-IV-22). Similarly, at Roseton, 

both in 1974 and in 1975, no eggs were collected at night in either the 

river samples or the plant samples assigned to the upper layer (Exhibit 

UT-3, Tables 3-1V725 and 3-IV-26). 

In all cases in which the seasonal plant and river densities are 

identical and non-zero, the w-ratio is equal to one. Therefore, it 

seems logical to us that whenever these densities are both equal to 

zero, the value of the w-ratio should be set equal to one. However, in 

all three of the above cases at Bowline and Roseton, LMS assumed a 

w-ratio of zero, implying no entrainment by the plants regardless of 

whether or not there actually were eggs present in the river. Dr. 

Englert of LMS offered two justifications for the assumption that zero 

divided by zero equals zero. First, he argued that it is valid to 

assume that w is equal to zero because the finding of no organisms in 

the intake samples indicates that none were being entrained (Transcript 

pp . 8383-85, emphasis added) : 

MR. KURENT: Since the river and the plant concentrations 

are identical, that is, in your ratio the numerator and the 

denominator for the equation are the same, shouldn't the w 

ratio logically be l? 

--\ 

_ -
v-59 

DR. ENGLERT: A zero divided by zero is an undefined 

quantity mathemat ica11y 

MR. KURENT: Undefined, but you define the w ratio as 

zero as a result of that calculation. 

MR. FRIEDLANDER: He had a remainder of an answer to 

give, his mouth was open. 

DR. ENGLERT: As we indicated, the w ratio is an 

estimate of the number of organisms entering the plant 

divided by the number of organisms out in the river. 

And keep in mind, of course, that we are using this 

w ratio in the computation of power plant impact. 

So that it would seem ridiculous to assume a value of 1 

for the w ratio which is going to be used to estimate this 

impact when, in fact, no organisms entered the plant. 

MR. KURENT: Isn't it equally as ridiculous to determine 

this equation to result in a zero? 

DR. ENGURT: No, it is not ridiculous for the very 

reason that I just explained. 

MR. KURENT: Shouldn't the mean concentrations actually 

be excluded if you don't choose to make the conservative 

assumption and call it a l? 

Since it is mathematically a like numerator and a like . 

denominator, shouldn't you either consider it a 1 or exclude 

this data in determining w ratios? 

DR. ENGLERT: NO. 

As I indicated in one of my previous answers, what we 

are trying to do here is to estimate power plant impact, and 

there is no reason to assume a w ratio of 1 when in fact we 

didn't find any organisms of that life stage at that plant in 

either the intake or discharge samples. 

MR. KURENT: How can you assume any w ratio on the basis 

of no organisms? 

Isn't the w ratio some measure of withdrawal or 

comparison of concentrations in the river and the plant? 

If you have no organisms, how can you use those zeros to 

give us a withdrawal ratio of zero? It is like making 

something from nothing; isn't it? 
V-60 

DR. ENGURT: The purpose of the w ratios as I have 

indicated several times is to aid us in estimating power 

plant impact. 

Now, as I have said repeatedly, now if we are trying to 

estimate the number of organisms entering the plant based on 

this w ratio, I see no basis for assuming a value of 1 when 

in fact we did not find any organisms in either the intake or 

the discharge of the plant. 

The fact that we didn't find any organisms means that 

there were none entering. 

Later, Dr. Englerr offered a second justification: since Roseton and 

Bowline are at the extreme ends of the spawning range of striped bass, 

no eggs were present in the river in the vicinity of these plants. We 

quote from Transcript pp. 8386-89 (emphasis added): 

MR. KURENT: Didn't you state earlier that the purpose of 

establishing criterion of wanting ten organisms per thousand 

cubic meters was to show that you were sampling at a time 

when in fact organisms were present in the river in 

significant concentrations? 

The fact that you have a zero concentration in the river 

and at the plant runs contrary to the stated purpose of 

computing the w ratio using the criteria that you established. 

DR. ENGLERT: As I recall, I said criterion were 

established to allow us to look at the period of appreciable 

abundance in either the plant or the river and to limit 

ourselves to those samples. 

But, in fact, what happened in these cases is that we 

note that there were eggs and/or larvae in the Hudson River 

during the sampling periods because these samples covered the 

entire period of abundance of those organisms. 

The fact that we didn't find them at the plant or the 

river simply means that they weren't in that particular plant 

region and in that particular plant. 

So I don't see any problem with assuming a value of zero 

in this case. 

MR. KURENT: Does the fact that you didn't find them in 

the river mean they weren't in the river? 

. -I 

,--, 
V- 61 

DR. ENGLERT: It means in terms of the data, it means 

that we didn't find them in our samples. 

MR. KURENT: Does the fact that you didn"t find them in 

the river mean that they weren't in the river? 

DR. ENGLERT: We know that during that same period there 

were organisms of that life stage of striped bass in the 

Hudson River. 

MR. KURENT: Does the fact that you didn't find them at 

the intake mean that they weren't entrained into the intake? 

DR. ENGLERT: It meaas that we did not capture any in our 

samples at the intakes. 

HR. KURENT: And you are not able to judge whether they 

were entrained into the intake; are you? 

DR. ENGLERT: I am not able to judge that based on the 

sampling program that was in effect during that time. 

MR. KURENT: You can assume, even though you found zero 

in the river, that there were still organisms present in the 

river; why can't you assume that even though you didn't find 

them at the intake that there were still organisms present 

going through the intake? 

DR. ENGLERT: Because I - because for the plants that 

we are looking at, the Bowline and Roseton plants which are 

on the extreme of the spawning regime of the striped bass, it 

is not unexpected to believe that there were no organisms in 

that plant region. 

I ssid that there were organisms of that life stage in 

the Hudson River as a whole. 

I say that based on OUK sampling program, we beli'eve that 

those organisms were not present in the Bowline or Roseton 

area at that time, or that year. 

We find both of Dr. Englert's rationalizations to be exceedingly 

unconvincing. First, the fact that no eggs were captured in the plant 

samples does not imply that no eggs were entrained (as Dr. Englert 

claimed in the emphasized quote on p. V-61). It simply means that none 

were collected in the tiny fraction of the intake water sampled by LMS 

(as Dr. Englert admitted in the emphasized quote on p. V-62). It 

clearly is possible that eggs were present but simply not collected. 
V- 62 

Given the pitifully inadequate sampling effort expended at the Roseton 

intake in 1974 (Section 4.3 of this testimony), it would have been 

surprising if any eggs had been collected. We noted in Section 4.3 

that the total volume of all the samples collected at the Roseton 

intake on dates used to compute 1974 w-ratios for striped bass waa only 

about 400 m 

3 

On the two dates used by LMS to compute 1974 w-ratios 

for eggs, May 9 and May 22 (Exhibit UT-3, p. 3-IV-511, only seven 

samples were collected at the plant and only 135 m 

3 of intake water 

were filtered (Table V-14). This total volume is approximately equal 

to the volume of a single river sample. 

Dr. Englert's second argument is flatly contradicted by the 

facts. Striped bass eggs were collected in the Bowline and Roseton 

regions in 1974 and 1975 by both LMS and TI. TI collected striped bass 

eggs in the Croton Haverstraw (CHI region, where the Bowline plant is 

located, and in the Poughkeepsie (PK) region, where the Roseton plant 

is located, both in 1974 and in 1975 (Exhibit UT-4, Tables 6.2-1 and 

6.2-5). During both years eggs were collected in every river region 

except Yonkers (YK). If striped bass eggs were in fact present 

+-. throughout the river, both above and below the Bowline and Roseton 

plants, then they must have been present in the vicinity of each 

plant. LMS' data shows that eggs were present at both locations, 

although they were not caught at all depths at all times of day. For 

example, at Roseton in 1974 and 1975 eggs were found in river samples 

collected at night in the lower layer and during the day in both upper 

and lower layers. Can it be that during the day eggs were present 

throughout the water column but at night they migrated to the bottom? 

Considering both TI'S and LMS' data, the only reasonable conclusion 

that can be drawn is that even though during some time periods at some 

depths no eggs were collected in the vicinity of the Bowline and 

Roseton plants, eggs were in fact present in the vicinity of the plants 

at those depths during those time-periods. 

Is the fact that eggs were probably present in the river, even 

though none were caught, further evidence that it is justifiable to 

assume that the w-ratio is equal to zero? Dr. Englett thinks so9 as 

evidenced by Transcript pp. 8417-23: 
V-63 

MR. KURENT: Since your sampling reflected the presence 

of no striped bass eggs in the river in the upper layer at 

night, can we then infer that if eggs were in fact present in 

the river in the vicinity of the plant, even though LMS' 

sampling program missed them, that it wouldn't be valid to 

assume then that zero over zero equals zero? 

DR. ENGLERT: NO. 

In the context of the answer that I just gave a few 

questions ago, that would in fact support the idea of a zero 

w ratio because, as I explained, any number divided by zero 

is in fact zero. 

So that if there were eggs there and we were able to 

measure that river concentration, if we divided that by the 

zero catch in the plant, we would get again zero. 

MR. KURENT: Let me restate the question and see if you 

can interpret it differently . I said, then can we infer that if eggs were in fact 

present in the river in the vicinity of the plant, even 

though your sampling program missed them, that it would not 

be valid to assume zero over zero to equal to zero; is that 

the question as you understand it? 

DR. ENGLERT: Yes, that's the question 5 just answered. 

MR KLRENT: If it can be shown that striped,bass were 

present in the river in significant numbers but you missed 

them, which you apparently did, wouldn't it be equally 

logical to assume that they were present in the vicinity of 

the intake and you missed them there as well? 

MR. FRIEDLANDER: Objection. 

You asked that same question yesterday. This is on 

page 8388. 

I will read the question and we will see the parallels 

between what was asked yesterday. 

MR. KURENT: I am asking a new question. 

MR. FRIEDLANDER: You are not. You are asking the same 

questions as you asked yesterday- It's on page 8388, lines 

I1 to 16. 

I would appreciate your distinguishing the difference 

between that question and the question you just asked. 

Talk about a waste of time. 
V-64 

MR. RURENT: Mr. Friedlander is correct that I asked that 

question yesterday, but I looked at the answer in the record 

that Dr. Englert gave, and the answer he just gave is 

different than the answer he gave yesterday and I would like ~ 

him to explain the difference. 

If I may read his answer from 8388. He said in response __ to the question yesterday: 

"Becayse for the plant that we are looking at, the 

Bowline and Roseton plants, which are on the extreme of the _-

spawning regime of the striped bass, it's not unexpected to 

believe that there were no organisms in the plant region." 

MR. FRIEDLANDER: And the answer continues. 

MR. KURENT: "I said that there were organisms of that 

life stage in the Hudson River as a whole. I say that based 

on our sampling program. We believe that those organisms 

were not present in the Bowline or Roseton area at that time 

or that year .I' 

I appreciate the reference, Mr. Friedlander. 

Now, that seems to differ with the answers you have 

given me this morning. So I am asking you again, if in fact 

there were organisms in the river contrary to your assumption 

in yesterday's answer -

DR. ENGLERT: First of all, I see no inconsistency in the 

answer. You are asking me now to work on a hypothetical, as 

I understand it. 

MR. K'3RENT: The hypothetical being? 

DR. ENGLERT: That there were organisms in the river 

which we did not capture. 

MR. KURENT: Perhaps I could assist you in that. Perhaps 

YOU could look at Exhibit 4, Table 6.2-5. 

Do you have a copy of that? 

DR. ENGLERT: I am sorry, I don't. 

JUDGE YCST: I have provided the witness with a copy of 

the exhibit. 
V-65 

MR. KUREW: Thank you, your Honor. 

That table, Dr. Englert, sets forth the regional 

densities of striped bass eggs during 1974 in the Hudson 

River estuary . 

Now, isn't the Roseton plant in the river section 

delineated by Poughkeepsie, PK? 

MR. FRIEDLANDER: Your Honor, I object to this line of 

questioning. These materials are on abundance and 

distribution, which was examined by Mr. Strong for two weeks. 

Dr. Englert is not particularly prepared to respond on 

these materials 

We were told that we would be questioned on W ratios 

today . 

JUDGE YOST: I will overrule your objection. I know what 

you are saying, but on the other hand I don't think it's 

improper to refer a witness to an exhibit in the testimony 

that speaks for itself. 

Be doesn't have to know about it to read the numbers. 

MR. KURENT: Dr Englert, if you look at the data 

presented under the column marked "Poughkeepsie," which is 

the Roseton plant vicinity, isn't it apparent that there were 

striped bass eggs recovered in various quantities throughout 

the sampling period around the Roseton plant during 1974? 

DR. ENGLERT: The figures that you are showing -- this 

table, 6.2-5, shows regional densities for the Poughkeepsie 

region, which extends seven or eight miles, as I recall. 

It's rather large region and according to that table, 

there were some eggs collected within thst region. 

MR. KURENT: How do you square this with the fact that 

you have assumed that there were no striped bass eggs in the 

Roseton plant region? 

DR. ENGLERT: I didn't assume that there were no eggs in 

the Roseton region. I computed a w ratio and I computed 

concentrations in the river in the immediate vicinity of the 

plant based on transsect sampling that was done by our 

company in the river immediately adjacent to .the Roseton 

power plant. 

And those results are presented in Table B-21. 
V-6 6 

MR. KURENT: Dr. Englert, if you would also look at the 

chart, isn't it apparent that in regions on either side of 

Poughkeepsie there were eggs present in various densities as 

well? 

DR. ENGLERT: Yes, that's true. 

MR. KURENT: Considering the fact that they appear to 

exist-in both regions on either side of Roseton and in the 

region in which Roseton is located, isn't it logical to 

assume that they existed in all these contiguous regions and 

therefore in front of the Roseton plant? 

DR. ENGLERT: As I am trying to explain, the sampling 

that we were basing the w ratios on is the transsect sampling 

and the sampling at the plant that was performed during 1974. 

Now, it wouldn't bother me at all if in fact we had found 

eggs in the river or if in fact there were eggs in the river 

during 1974 because the important point is that we didn't 

find any in the plant. 

And in fact, it would simplify our discussions if there 

were some eggs in the river because it would mean that we 

have a non-zero number to divide into the zero that we found 

in the plant and it would still give us the result, which is 

a zero w ratio. 

Dr. Englert's argument is fallacious. If eggs were present in the 

river but not collected, then eggs may just as well have been present 

at the plant but not collected there either. In fact, since the 

sampling effort at power plant intakes is nearly always substantially 

less than that at the corresponding river stations, it is much mere 

likely that no eggs would be collected at a power plant intake, even 

though eggs were being entrained, than it is that no eggs would be 

collected in the river, even though eggs were present thereNo eggs were collected at the Roseton intake in 1974. We have 

previously noted that only 135 m3 of water were filtered at the 

Roseton intake on the two sampling dates in 1974 that met LMS' criteria 

for w-ratio analysis for striped bass eggs. In Table V-17 we present 

5 the volume (m ) of the river samples collected on these dates, and 

the number of these samples that contained striped bass eggs. Even 

. ­

.-

though the average volme of each sample collected from the river on 

these two dates was greater than the total volume sampled at the 

I 
V-67 

Table V-17. Numbers of samples collected at Roseton/Danskamner river 

transect stations on May 9 and May 21, 1974a that contained striped bass eggs 

Average

sample Number of Number of samples Date Nb volume (m3) eggs collected containing eggs 

May 9 36 199.1 11 1 

May 21 37 202.6 39 3 

aData collected on these dates were used by LMS to calculate w-ratios 

for striped bass eggs. 

bTotal number of samples collected at all stations and depths during

daytime and nighttime. 
V- 68 

intake on both dates combined, only 1 out of 36 (3%) river samples 

collected on May 9 and only 3 out of 37 (8%) collected on May 21 

actually contained eggs. Clearly, even if the density of eggs in water 

entrained by the Roseton plant on these dates was in reality equal to 

the density of eggs in the river, the probability that any would be 

collected in the 19 m3 of water sampled on May 9 or the 116 m

3 

sampled on May 21 (Table V-14) is negligible. ,. .._ 

We believe that LMS' assumption that zero divided by zero is equal 

to zero is unjustifiable. W-factors are not measures of the numbers of 

organisms entrained by power plants, but measures of the density of 

organisms in power plant cooling water relative to their density in the 

river in the vicinity of the plant. If no organisms are collected at 

either location, the most reasonable assumption is that the plant and 

river densities are equal. Therefore W-factors should be set equal to 

one. 

Since we do not compute a W-factor unless at least ten organisms 

were collected in either the plant or the river samples, we do not 

encounter this problem when calculating W-factors using the MU method. 

A similar problem does occasionally occur in the application of the GBC 

method: if the computed values of the upper and lower layer w-ratios 

are both zero, then the value of the factor A computed from Eq. (V-10) 

(Section 2.2.2), i.e., (wu - wL)/(wu + w,>, is equal to zero 

divided by zero. Interestingly, in this case we assume that A is equal 

to zero, for the same reason we set forth above when arguing that LMS' 

w-ratios should be set equal to one. If wU and wL are equal and 

nonzero, then A is equal to zero. Thus, zero divided by zero is equal 

to one, but zero minus zero divided by zero is equal to zero. ,^ 
V-69 

5. DISCUSSION 

Even a casual inspection of Tables V-3 through V-12 reveals that 

many of the GBC and MU W-factors computed for the same population, 

life-stage, year, and time of day differ substantially from each other. 

This is not unexpected, given the differences in assumptions and 

computational procedures between the two methods. The HU method assumes 

that the gears used to collect plant and river ichthyoplankton samples 

have equal collection efficiencies. All differences between observed 

plant and river densities are attributed to real differences between 

the abundance of organisms in a power plant intake and their abundance 

in the river cross-section in front of the plant. The same is not true 

of the GBC method. The GBC method attributes all differences between 

the abundance of organisms in the upper layer of an intake bay and their 

abundance in the upper layer of the river to the effects of gear bias. 

It is the relative magnitudes of upper and lower layer w-ratios and the 

vertical stratification of organisms in the river that determine the 

value of a W-factor calculated using the GBC method. 

' Because of the differences between the two methods, violations of 

assumptions that affect one will not affect the other. If, for some 

population and life-stage, the efficiency of the gear used to collect 

samples at a particular plant is markedly different from the efficiency 

of the gear used to collect the corresponding river samples, a bias 

will be introduced into the MU W-factor but not into the GBC W-factor. 

Conversely, if there is a lateral trend in the distribution of 

organisms belonging to a given species and life-stage in the upper 

layer of the river (e.g., they are more abundant near the east or west 

shore than in the channel), the GBC W-factor will be affected but the 

Mu W-factor will not. Equally important, and for exactly the same 

reasons, the two methods are affected differently by sampling errors. 

If, by chance alone, the observed density of organisms of a particular 

population and life-stage in the river samples is lower than the 

observed density in the plant samples, although the true plant and 
-- 

v- 70 

river densities are the same) then the MU W-factor will be erroneously 

high. The GBC W-factor computed from the same data may be erroneously 

high, erroneously low, or unaffected, depending on how the relative 

magnitudes of wU and wL, and the fractional distribution of 

organisms in the upper and lower layers of the river are affected by 

the sampling error. 

Since each run of the Empirical Transport Mode1 employs 20 

U-factors (five plants x four life-stages), the differences observed 

within individual GBC/MU pairs are unimportant as long as neither 

method produces consistently larger or smaller W-factors than does the 

other. Table V-18 presents a comparison of the GBC and MU W-factors 

for striped bass, white perch, Atlantic tomcod, bay anchovy, and 

-Alosa. For each population we tabulated the number of times the GBC 

W-factors were larger than the corresponding MU W-factors. We then 

used the sign test (Siege1 1956) to determine whether W-factors for any . 

of these species calculated using the GBC method are on the average 

larger or smaller than similar W-factors calculated using the MU method. 

The MU W-factors for bay anchovy are consistently larger than the 

GBC W-factors. This result indicates that for this species one (or 

both) method(s) is subject to some kind of systematic bias. 

Unfortunately, we cannot determine the nature of this bias, and 

therefore we have no way of knowing which set of W-factors corresponds 

most closely to reality. The practical effects of the systematic 

difference between the two sets are an increase in the range of 

conditional entrainment mortality rates obtained from the Empirical 

Transport Model, and a concomitant increase in the uncertainty of 

conclusions drawn about the impact of entrainment on the Hudson River 

bay anchovy population. 

For the other four species, the sign test revealed no significant 

difference between the number of GBC W-factors that are larger than the 

corresponding MU W-factors and the number that are smaller. Thus, for 

striped bass, white perch, Atlantic tomcod, and Alosa there is little 

overall difference between the two sets of W-factors. Similar 
V-71 

Table V-18. Statistical significance of differences between 

the MU and GBC W-factors for each species, as 

determined from . the si gn testa 

Number of 

Speci es paid nf . Pd 

Striped bass 50 25 1.00 (NS) 

White perch 48 22 0.67 (NS) 

At 1anti c tomcod . 21 10I 1.00 (NS) 

Bay anchovy 30 6' 0.01e 

Alosa spp. 43 16'. 0.13 (NS) 

aMethod described by Siege1 (1956). 

bNumber of pairs of MU and GBC W-factors calculated for the 

same species, life-stage, plant, year and time of day, excluding ties. 

cNumber of pairs in which GSC W-factor is larger than MU 

W-f actor. 

dTwo-tai 1ed test . 

esignificant at 1%level. 
V-72 

conditional entrainment mortality rates should be obtained from the 

Empirical Transport Model regardless of which method supplies the input 

parameters. 

For four out of five species, independent methods of solving the 

same problem (i-e., how to estimate a W-factor), each involving 

different simplifying assumptions and subject to different sorts of 

biases, have led to similar results. Because the conditional 

entrainment mortality rates for striped bass, white perch, Atlantic 

tomcod, and Alosa produced by the ETM are relatively independent of the 

method used to generate W-factors, they should be, in Levins' (1966) 

terminology, "robust" with respect to these parameters. For these 

species conclusions drawn from the ETM results, and management 

decisions based on those conclusions, should be relatively free from 

the effects of the various types of error6 that can affect estimates of 

W-factors and w-ratios. 

.,.' 

t 
v-73 

6. REFERENCES CITED 

Barnthouse, L. W., J. B. Cannon, S. W. Christensen, A. �I.Eraslan, 

J. L. Harris, K. H. Kim, M. E. Laverne, H. A. McLain, B. D. 

Murphy, R. J. Ziaridon, T. H. Row, R. D. Sharp, and W. Van Winkle. 

1977. A selective analysis of power plant operation on the Hudson 

River with emphasis on the Bowline Point Generating Station. 

ORNL/TM-5877. Oak Ridge National Laboratory, Oak Ridge, Tennessee. 

Carpenter, E. J. 1979. Sampling ichthyoplankton. Exhibit EPA-196. 

Cassie, R. M. 1963. Microdistribution of plankton. Oceanogr. Mar. 

Biol. Ann. Rev. 1:223-52. 

Ecological Analysts. 1976a. Bowline Generating Station entrainment 

survival and abundance studies. 1975 Annual Interpretive Report. 

Prepared for Orange and Rockland Utilities, Inc. 

Ecological Analysts. 1976b. Lovett Generating Station entrainment 

survival and abundance studies. 1975 Annual Interpretive Report. 

Prepared for Orange and Rockland Utilities, Inc. 

Exhibit UT-3. Consolidated Edison. 1977b. Supplement I to Influence 

of Indian Point Unit 2 and other steam electric generating plants 

on the Hudson River estuary, with emphasis on striped bass and 

other fish populations. 

Exhibit UT-4. Consolidated Edison. 1977a. Influence of Indian Point 

Unit 2 and other steam electric generating plants on the Hudson 

River estuary, with emphasis on striped bass and other fish 

populations. 

Exhibit UT-6. Central Hudson. 1977. Roseton Generating Station. 

Near-field effects of once-through cooling system operation on 

Hudson River biota. 

Exhibit UT-7. Orange and Rockland. 1977a. Bowline Point Generating 

Station. Near-field effects of once-through cooling system 

operation on Hudson River biota. 

Exhibit UT-7A. Orange and Rockland. 1977b. Appendixes-Bowline Point 

Generating Station. Near-field effects of once-through cooling 

system operation on Hudson River biota. 
v-74 '7 

Levins, R. 1966. The strategy of model building in population biology. .1 

Am. Sci. 54(4):421-31. .~ New York University. 1976. Effects of entrainment by the Indian Point 

power plant on biota in the Hudson River estuary. Progress Report 

for 1974. Prepared for Consolidated Edison Co. of New York, Inc. 

New Pork University. 1977. Effects of entrainment by the Indian Point 

power plant on biota in the Hudson River estuary. Progress Report 

for 1975. Prepared for Consolidated Edison Co. of New York, Inc. 

Siegel, S - 1956. Nonparametric Statistics for the Behavioral Sciences. 

McGraw-Kill, New York. 312 pp. 

Smith, P. E., R. C. Counts, and R. I. Clutter. 1968. Changes in 

filtering efficiency of plankton nets due to clogging under two. 

J. Cons. Perm. Int. Explor. Mer 32(2):232-42. 

Texas Instruments. 1975. Indian Point impingement study report for the 

period 1 January 1974 through 31 December 1974. Prepared for 

Consolidated Edison Company of New York, Inc. Exhibit EPA-92B. 

,-. 

J